{"as_of":"2026-08-18T03:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e17f62d7cab81d105e6eba6038f74aabbfb04e86341820846b2235b1ad52d286","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:58:41.157677Z","state":"measured"},{"denominator":84,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":84,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:34:54.803404Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T23:35:01.421722Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"cited_work":{"arxiv_id":"2501.13967","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.13967","snapshot_observed_at":"2026-08-06T23:35:01.421722Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","venue":"cs.CV","work_id":"abf10653-7c9d-44ed-9d9c-a9ebbbfd0a2d","year":2025},"citing_paper":{"arxiv_id":"2506.17562","last_updated":"2025-07-18T07:39:50Z","snapshot_observed_at":"2026-08-14T09:45:08.158750Z","submitted_at":"2025-06-21T03:13:08Z","title":"LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:34:54.803404Z"},"links":{"cited_paper":"/paper/2501.13967","citing_paper":"/paper/2506.17562"},"observation_digest":"sha256:39862a8abc97e5376d6191ff661fd7a2e85936149616bacd7accdbb32dd35613","observation_id":"048b8118-fad9-4549-a441-74674ec192ed","resolution":{"observed_at":"2026-08-06T23:35:01.513189Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.13967/citation-record","integrity":"/paper/2501.13967/integrity","json":"/paper/2501.13967/citation-record.json","paper":"/paper/2501.13967"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.085846Z","title":"Deep learning-based image quality as- sessment for optical coherence tomography macular scans: a multicentre study,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.085846Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:1d9167048173a4ebeb64197afa7010089065194605970054d08aad74a9a8f198","observation_id":"9a93d0a7-be42-46c6-88e2-1007ff183afd","resolution":{"observed_at":"2026-08-10T16:58:40.085846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.156873Z","title":"Using deep learning for assessing image- quality of 3d macular scans from spectral-domain optical coherence tomography,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.156873Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:dcc8d950f8d60e601a3541730ed634b5f759049b40909a977e0db7d6a3e671a8","observation_id":"423f29a6-6d2d-411d-b846-aa20a09ed5f5","resolution":{"observed_at":"2026-08-10T16:58:40.156873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.225267Z","title":"The future of digital health with federated learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.225267Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:6801fbdaebc529780be8e1d95280b9b5c71dbf69ef1b8919da4f99cd286b8098","observation_id":"ae65e791-1158-47dd-a47d-0c209313001a","resolution":{"observed_at":"2026-08-10T16:58:40.225267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.352358Z","title":"Regulation (eu) 2016/679 of the european parliament and of the council,","venue":null,"work_id":"d5eff46a-923f-4d50-988c-753eb5e1493e","year":2016},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.239792Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:f511f888a952d87e7884486ea1bcf38168fd1aeffd32447d22559d0853c9ff9a","observation_id":"25e388ad-35ff-42ea-830e-320d7496ea44","resolution":{"observed_at":"2026-08-10T16:58:44.357592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.244330Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.244330Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:19049a062e2604daff99e060a4ddd3a79160cfb78f839417f2d8af2baf35fee9","observation_id":"6c13b1d2-61f5-4eb5-97d7-12e6c84bc482","resolution":{"observed_at":"2026-08-10T16:58:40.244330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.292064Z","title":"Out-of-distribution generalization of federated learning via implicit invariant relationships,","venue":null,"work_id":"71ae3463-f513-4cca-a1ee-9256bb7e6214","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.249926Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:d097927acafbb9d713a22939bd84e3ce7af88afc355e57b6f53ecb0db3a8e2b5","observation_id":"863d12ee-4aa3-49eb-b1d0-3f155d2f8363","resolution":{"observed_at":"2026-08-10T16:58:44.329855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.221717Z","title":"Anomaly detection under distribution shift,","venue":null,"work_id":"324c0fa6-b9c0-4efb-a2e8-1a61b6646a0d","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.255399Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:f065f315c1b5c042bef1757fdbb9217c2330ec62085088f9f8ba6bd71056ace9","observation_id":"ffa03304-070f-4165-bbf4-972535498307","resolution":{"observed_at":"2026-08-10T16:58:44.239148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.208011Z","title":"Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,","venue":null,"work_id":"a7b3360e-dc61-46df-ba21-100294c8e88c","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.260137Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:b1233ba678e3e182323ac3cb9a299273e1cffa9f82ac1a286e417c418456d154","observation_id":"3503579c-068c-4756-8f20-cf01058cc643","resolution":{"observed_at":"2026-08-10T16:58:44.212470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.193152Z","title":"A deep learning system for predicting time to progression of diabetic retinopathy,","venue":null,"work_id":"25588efb-f0a3-4c51-a0fe-ef10c40c8b48","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.264927Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:956f9846d595657487e20387644fd37d473eb572bfe6127dcd21e850d28953ac","observation_id":"54693709-0d56-4476-ba5a-1d98bbd23843","resolution":{"observed_at":"2026-08-10T16:58:44.197943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.178876Z","title":"Unsupervised domain adaptation for anatomical landmark detection,","venue":null,"work_id":"0174ba2f-ce2f-4857-8679-44f0be65790d","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.269537Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:cb001e4b8a2d06910b27794bf1bbc246a1b76c699aca01a9b992944208e68cf7","observation_id":"c6ee1c2e-e3d9-42ed-830e-da5248a816f2","resolution":{"observed_at":"2026-08-10T16:58:44.183780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.164399Z","title":"Federated domain general- ization for image recognition via cross-client style transfer,","venue":null,"work_id":"616c9cd0-b294-4f8a-9ec8-1693d9a91e56","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.275869Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:4cb865adba065e7653e1a1ec7fd67c6190a1bef0bc4f942c4c5a8414b3bd8716","observation_id":"47cbf7c9-bb8c-4d27-8706-e75e433d5529","resolution":{"observed_at":"2026-08-10T16:58:44.168784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.150371Z","title":"Efficient federated domain translation,","venue":null,"work_id":"82bd9b40-f898-4554-aefe-5a5e049cba55","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.280202Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:77f61b1ff5dff217d4502f1c7c37bf414bfa4c6ebe2782301c2b5497eb3fcfe4","observation_id":"1152ab21-e3f5-415e-8ae0-fe65cb124cf5","resolution":{"observed_at":"2026-08-10T16:58:44.155103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.097724Z","title":"Stablefdg: Style and attention based learning for federated domain generalization,","venue":null,"work_id":"5bcd695a-4ffb-40c6-b447-f09ff9bbd70d","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.284239Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:5d50b6561eead59d98a958b7d06b61fed163283467e36fe1fa54c62ebec81781","observation_id":"044a82fa-7be8-4be3-b5aa-6be7d720af01","resolution":{"observed_at":"2026-08-10T16:58:44.139489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.025209Z","title":"Federated adversar- ial domain hallucination for privacy-preserving domain generalization,","venue":null,"work_id":"ae3213b4-a792-490a-b0e1-df3d47ae06a4","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.288570Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:d2689ffe762505330619f4c68d040f9bc2cea5891da137361e18be8369924712","observation_id":"144f3022-c9c8-4681-a29d-079e3697d080","resolution":{"observed_at":"2026-08-10T16:58:44.067471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:44.005988Z","title":"Fedsr: A simple and effective domain generalization method for federated learning,","venue":null,"work_id":"77b02816-a796-4f07-8bc5-4f5faff95fc5","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.293642Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:1949a76407ee3952e500418667b58f2ff122e257b874df9a61ec0977b85284ff","observation_id":"66fbf098-6888-461c-94c9-b5208b4d758d","resolution":{"observed_at":"2026-08-10T16:58:44.010432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.991058Z","title":"Closing the generalization gap of cross-silo federated medical image segmentation,","venue":null,"work_id":"b39dde0c-e3af-4f92-85cf-7200d8ee891e","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.297662Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:e241eeb82c9d90cda0ce1f11f4de583583efdbb3b792135a7c37e385d4f00617","observation_id":"2110de1b-b5dc-4644-b069-d3772632dd76","resolution":{"observed_at":"2026-08-10T16:58:43.995586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.978014Z","title":"Federated learning for iot devices with domain generalization,","venue":null,"work_id":"7c470e58-b2ae-49a6-aa40-657ea53ac4dc","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.301691Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:22663c235a2383bea7b2d570b7034b78fcda37ba04eb23661bcf0863dbee7570","observation_id":"aacc3b97-0943-401a-84ab-cdf5bc55a772","resolution":{"observed_at":"2026-08-10T16:58:43.982074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.962871Z","title":"Federated domain generalization with generalization adjustment,","venue":null,"work_id":"37c59ceb-26e4-45a3-a99c-f8675da9a68e","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.305860Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:274864f55ca49c42784a7833ec003e58594b52b622237f5efe5f0f0278b27bac","observation_id":"8321a7a4-666e-4579-bca2-4d0fc01ff061","resolution":{"observed_at":"2026-08-10T16:58:43.968003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.947886Z","title":"Collaborative semantic aggregation and calibration for federated domain generaliza- tion,","venue":null,"work_id":"18b8b961-678c-4655-9731-1f3f77e774ca","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.310423Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:e78c7d7c41360fd22bfe64fca2451a4038c2e8f697c85c901b5ecb96d63f36e3","observation_id":"8cf327f4-a261-4690-bc98-4c60862bbe26","resolution":{"observed_at":"2026-08-10T16:58:43.953592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.933656Z","title":"Iop-fl: inside-outside personalization for federated medical image segmentation,","venue":null,"work_id":"6c5b8413-21c0-4543-833b-2abad2f3e7f7","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.314737Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:338c20931823d658dbc8d780cd3074c8ad0228f1840d8a71d67ec7b11feaf874","observation_id":"1736d22c-20f9-4466-9e8c-ecbf23b70c3f","resolution":{"observed_at":"2026-08-10T16:58:43.938185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.772843Z","title":"Domain-aware dual attention for generalized medical image segmentation on unseen domains,","venue":null,"work_id":"0f509942-7f30-4774-8f4e-43bb5d5a9913","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.319600Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:44680d8ad2bc4c1468d2e4a3d6ca8729d4a7c9901f836120ced65822d4ddb5eb","observation_id":"65e25f4e-06f0-4e9e-8cf1-8d2a3539af6d","resolution":{"observed_at":"2026-08-10T16:58:43.846095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2403.15605","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.492191Z","title":"Efficiently assemble normalization layers and regularization for federated domain generalization,","venue":null,"work_id":"b1e55660-6e1f-4e0b-a1e2-f708b2e57db1","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.323931Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:093f3611bca7ee5d712dc8f978bd968b8533bc05743cc3a80143f0b977c393fc","observation_id":"2e382162-4851-45d7-ab85-e1c4e0b092f1","resolution":{"observed_at":"2026-08-10T16:58:41.524849Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.757303Z","title":"Rfdg: Reinforcement federated domain generalization,","venue":null,"work_id":"38ed5314-c578-4659-a21e-519f4c417042","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.328007Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:08e7a2574e6124e80eb0227cfb9e4e5c4f943c265ac33b86b465ebc5d47e6ada","observation_id":"6bcbf8e6-d358-48ea-ac81-6f07f8ee5f02","resolution":{"observed_at":"2026-08-10T16:58:43.762339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.742327Z","title":"Fraug: Tackling federated learning with non-iid features via representation augmentation,","venue":null,"work_id":"f909304d-137d-4b30-a670-a63f3af989ff","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.332611Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:665e8f446c229ecb277482dd8980273ca5ebe5751c5ee01fd5c4b4e691306039","observation_id":"a7863322-725e-4893-bc3d-7b04601cb663","resolution":{"observed_at":"2026-08-10T16:58:43.747380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.622816Z","title":"G2g: Generalized learning by cross- domain knowledge transfer for federated domain generalization,","venue":null,"work_id":"a7370771-1504-470c-8ba9-2e559848e92f","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.337158Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:b2f6b0231375a319b1705d292b146c8d891f08ccba1b9bfbee707a7503a221ae","observation_id":"4d1dd5a5-5fca-4cab-8944-4893e7014124","resolution":{"observed_at":"2026-08-10T16:58:43.694985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.460515Z","title":"Beyond the federation: Topology-aware federated learning for generalization to unseen clients,","venue":null,"work_id":"18e2f6cd-ea23-44c5-ae9e-8609ea5f9b1c","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.341422Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:a453b6e0e9f0b8ced40ce45395b37684aff4c6c24d3b9d28feac6f50e4e62269","observation_id":"0643d39f-e999-405b-99d0-4316ae26346a","resolution":{"observed_at":"2026-08-10T16:58:43.513538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.444896Z","title":"Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning,","venue":null,"work_id":"5827e93b-43a2-4975-843f-6305ab33f2bc","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.345423Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:f41cfab17593be67c58fec5ab1407c7b80958baa57c9e3993627d3b3c94eb87d","observation_id":"0e850f77-0dfd-4450-9262-7cf168ee0825","resolution":{"observed_at":"2026-08-10T16:58:43.450098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.429323Z","title":"Dafkd: Domain- aware federated knowledge distillation,","venue":null,"work_id":"575cdfb4-857f-4716-bbe9-1c575f67b4f2","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.349943Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:a9a8934b77bde337aa1f9adc28fbb3f7a95922993b725d7b82ab699b73921346","observation_id":"5a50026a-e4fa-4055-9b7a-9bea277dd0b5","resolution":{"observed_at":"2026-08-10T16:58:43.434353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.415460Z","title":"Learning to generate novel domains for domain generalization,","venue":null,"work_id":"ac734585-4a99-4444-a232-cd006cb87170","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.354097Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:9ba44087b091c26d49e2d60de8542e2a61dca47456e058c7e3551757195d7892","observation_id":"59fdd27a-6034-4b44-97c0-0d4ea272b38f","resolution":{"observed_at":"2026-08-10T16:58:43.420063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.401013Z","title":"Deep domain-adversarial image generation for domain generali- sation,","venue":null,"work_id":"f9a5ae32-fced-48cf-92f5-b5f58d22ce4a","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.358508Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:b6154f821ca41d11c9de02089b14ed87390bc38e2bfb7cd6e55b02de5f021241","observation_id":"53681ea6-0d6f-41bb-9e26-ce7f5cae327b","resolution":{"observed_at":"2026-08-10T16:58:43.405571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.255531Z","title":"Adversar- ial teacher-student representation learning for domain generalization,","venue":null,"work_id":"4a79b2d0-20eb-4ad0-996f-88d663068a85","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.364123Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0af43b304c3bf599bafaf97b62286a072ffc5e3bb2d713edd34f03808e1ad457","observation_id":"99407969-00e8-4f12-a5ac-c535e53000fb","resolution":{"observed_at":"2026-08-10T16:58:43.278942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.239284Z","title":"Learning to aug- ment via implicit differentiation for domain generalization,","venue":null,"work_id":"119ff840-1de6-4bae-a367-c332444343d7","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.368251Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:302114f2bb075361eb99a08c9258f451ad20b94a162d6c031468b657c293b2ad","observation_id":"9b8b668a-e810-46ff-98e9-b63f52f9be75","resolution":{"observed_at":"2026-08-10T16:58:43.244580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.402717Z","title":"Wasserstein generative adver- sarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.402717Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:96821105240f31cdb1132b8f5ea0d961a31a67c81a94999bba3b25fdd1bc8c3c","observation_id":"9fdd6e35-b16b-4e9c-81cf-d3d67a5dced8","resolution":{"observed_at":"2026-08-10T16:58:40.402717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.04948","last_updated":"2019-03-29T11:08:46Z","snapshot_observed_at":"2026-08-14T17:44:45.577553Z","submitted_at":"2018-12-12T13:59:43Z","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.04948","snapshot_observed_at":"2026-08-10T16:58:40.444354Z","title":"A style-based generator architecture for generative adversar- ial networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.444354Z"},"links":{"cited_paper":"/paper/1812.04948","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:5a74796c6704ce2abcbb87a9556b79e99265147661ae4251746ffb1e5f23f526","observation_id":"379e7387-fcc5-4079-8279-3334f1baa60f","resolution":{"observed_at":"2026-08-10T16:58:40.444354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.525883Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.525883Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:76cbb0b7f0acd0d7dfc4b260181fae1d29e2df5841621c80ce0a5dec56d0e5ae","observation_id":"12cd72db-db5a-4fcb-9492-95bf915a7048","resolution":{"observed_at":"2026-08-10T16:58:40.525883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.573002Z","title":"Improved denoising diffusion proba- bilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.573002Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:c510d46efc4cc5b5ea3b8bc45fa370430840c97771fc115de1c1b4601b51a040","observation_id":"96166baa-4faa-4215-84b4-8dd2b35db5cd","resolution":{"observed_at":"2026-08-10T16:58:40.573002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.197649Z","title":"High-resolution image synthesis with latent diffusion models,","venue":null,"work_id":"d0935adb-90c9-471f-8cde-92b3dbf84c61","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.608172Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:bfb7377214c282ecd6f4d6e13a1ffa1329d0f82202bfdddb702493d0e9f854f3","observation_id":"44fc04e3-2aed-4837-aeba-498d24124f91","resolution":{"observed_at":"2026-08-10T16:58:43.202240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.132895Z","title":"Unpaired optical coherence tomog- raphy angiography image super-resolution via frequency-aware inverse- consistency gan,","venue":null,"work_id":"ba5ecd64-126a-474e-8405-435d74c7465c","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.670962Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0660681ebbe60fa7a7be77b9db78d90f2fec329091a25afa1c70c7120ba31e63","observation_id":"c953cec9-95f5-4cef-acdf-a4d20eeb1681","resolution":{"observed_at":"2026-08-10T16:58:43.188372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.698491Z","title":"Towards generalizable diabetic retinopathy grading in unseen domains,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.698491Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:521065d1ed4ac909047b0f563742c44208e0b55ade9851e1593dd499a809f2cb","observation_id":"1445e63d-2f79-4330-976d-cbcdb690716f","resolution":{"observed_at":"2026-08-10T16:58:40.698491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.702877Z","title":"Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.702877Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0ef10ba6780839b46465a66ba37f4f8be939540b9cacbb7d107bb2bec0b6d89a","observation_id":"cccf08d1-3c18-4fda-9d80-df315b4d38af","resolution":{"observed_at":"2026-08-10T16:58:40.702877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:43.017108Z","title":"Domain general- ization: A survey,","venue":null,"work_id":"7cf2fee3-662e-4610-8776-0b7202f7bb3c","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.707667Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:eb83573a1fcbec8a045906dcb1de89bfcb23ec48d58fc2bb0f3c5b0cfddf48ac","observation_id":"cf1aaa21-642f-4071-9840-af2edf19b68f","resolution":{"observed_at":"2026-08-10T16:58:43.029332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:40.711468Z","title":"Image quality-aware diagnosis via meta- knowledge co-embedding,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.711468Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:875358fcd5cb4a19e916d51c9077ba02b705a1b2e719d2a27ea3b7d716a7d0c1","observation_id":"35cb2199-41d6-4696-a999-d507c6b6b379","resolution":{"observed_at":"2026-08-10T16:58:40.711468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.992338Z","title":"The effect of intrinsic dataset properties on generalization: Unraveling learning differences between natural and medical images,","venue":null,"work_id":"5c421d85-f42b-4d81-8ff1-bdba8e1ee816","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.716390Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:9bdc9e344cdaeec712153c99968fe13c3f74e99e2d305916d8396111b3dca86b","observation_id":"09c8dcd7-09a7-4549-8620-65547f268aae","resolution":{"observed_at":"2026-08-10T16:58:42.997497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02389","last_updated":"2023-04-05T12:04:55Z","snapshot_observed_at":"2026-08-16T15:42:34.976753Z","submitted_at":"2023-04-05T12:04:55Z","title":"DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images","version":1},"cited_work":{"arxiv_id":"2304.02389","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.02389","snapshot_observed_at":"2026-08-10T16:58:41.397923Z","title":"DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images","venue":"eess.IV","work_id":"2c018d4c-7fd1-4720-a4db-999ac5064726","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.721020Z"},"links":{"cited_paper":"/paper/2304.02389","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:89bac13d59b17ba993f1144e22021033b6cb10fbcdf7a1303bf2a026cfe27b38","observation_id":"dfe4bee3-d7e5-4d81-bc47-958e4ec590e3","resolution":{"observed_at":"2026-08-10T16:58:41.403306Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04556","last_updated":"2024-09-16T02:02:15Z","snapshot_observed_at":"2026-08-16T14:03:10.053629Z","submitted_at":"2024-04-06T08:45:07Z","title":"Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach","version":2},"cited_work":{"arxiv_id":"2404.04556","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.04556","snapshot_observed_at":"2026-08-10T16:58:41.374993Z","title":"Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach","venue":"cs.CV","work_id":"0b9b67b3-aaf0-4ba6-85dd-8ab1b2c209f7","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.726071Z"},"links":{"cited_paper":"/paper/2404.04556","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:c3df0991187a62d3756495c9bfd81d22b183138d2fcabbaad77fa7ddc75d264e","observation_id":"5345d4e0-d14b-4a6b-a4c5-8a3953382735","resolution":{"observed_at":"2026-08-10T16:58:41.382050Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.976281Z","title":"What do we mean by generalization in federated learning?","venue":null,"work_id":"223e98f3-bea7-400b-8133-36541e788deb","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.730230Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:f88405c0163864c0f62f98d56c7ce21170b0bc6608391d8c2d1eeff92236587e","observation_id":"38b9118b-21f4-4ea6-be1a-134dbff2e13f","resolution":{"observed_at":"2026-08-10T16:58:42.981803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.960886Z","title":"Unified deep supervised domain adaptation and generalization,","venue":null,"work_id":"57a4831a-69e0-4660-bed3-c45103084609","year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.734845Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0e53804bf4a31d9af2751db68acacfcc5c75582d51185a3a77b1916698b49f22","observation_id":"798f4a4d-d5fc-4ab0-b5ac-7e8758d6e7d3","resolution":{"observed_at":"2026-08-10T16:58:42.965970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.946906Z","title":"Multi-adversarial discriminative deep domain generalization for face presentation attack detection,","venue":null,"work_id":"74a62955-609d-4287-b9f7-a90714efbb38","year":2019},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.740170Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:66863d6c25845a4d54135846b49bdce1c0ed11648ef89c0fbc6510b4af84cd84","observation_id":"b895c3d1-fcc5-4bb5-b52f-3dd9110e3ff3","resolution":{"observed_at":"2026-08-10T16:58:42.951643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.794784Z","title":"Do- main generalization via model-agnostic learning of semantic features,","venue":null,"work_id":"f3160e66-3496-4ecd-9a8d-b4f629bb8618","year":2019},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.744660Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0c92acd363ec28bf564903cbdf3f275827285b03bb6a20c74c991ab16615b866","observation_id":"ea38e3f9-60a6-416b-b8c4-453be0102999","resolution":{"observed_at":"2026-08-10T16:58:42.864898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06750","last_updated":"2023-11-12T06:32:30Z","snapshot_observed_at":"2026-08-16T14:44:04.417681Z","submitted_at":"2023-11-12T06:32:30Z","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06750","snapshot_observed_at":"2026-08-10T16:58:40.749681Z","title":"Federated learning for generaliza- tion, robustness, fairness: A survey and benchmark,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.749681Z"},"links":{"cited_paper":"/paper/2311.06750","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:72674e4ff772b856ef5fda0b214579ad730ecad378cfca771b72692ffc21b527","observation_id":"ac2f43f4-3aee-4ca6-b514-907076ba0280","resolution":{"observed_at":"2026-08-10T16:58:40.749681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.628307Z","title":"No one left behind: Real-world federated class-incremental learning,","venue":null,"work_id":"839c9066-8e64-48bb-8e32-eafc8d58bd08","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.754668Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:c7e5a89d0237d2a4f31a363093dc48daf27d06123c642ff6709b7b99d9ec7f0b","observation_id":"6c83366b-f087-412c-ac81-74cc64a32a16","resolution":{"observed_at":"2026-08-10T16:58:42.681373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.614439Z","title":"Fedseg: Class-heterogeneous federated learning for semantic segmentation,","venue":null,"work_id":"f3a72155-c56f-4fd7-952f-b4c3a119bc4a","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.759670Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:abc21933701b51702708d043e303e748ea1ca3164d6ed1ad17e057fac477c7c5","observation_id":"d83825ad-7f2d-49c6-81c1-baec7341c824","resolution":{"observed_at":"2026-08-10T16:58:42.619144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07925","last_updated":"2024-05-13T16:57:48Z","snapshot_observed_at":"2026-08-16T13:53:05.524230Z","submitted_at":"2024-05-13T16:57:48Z","title":"Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07925","snapshot_observed_at":"2026-08-10T16:58:40.764466Z","title":"Stable diffusion- based data augmentation for federated learning with non-iid data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.764466Z"},"links":{"cited_paper":"/paper/2405.07925","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:2f93077bad2f41e704a5986359e4952beb283028112d40d1c2ad0ad066f8eda2","observation_id":"b02c9a74-fac6-4b9f-b3b3-27845e4205f1","resolution":{"observed_at":"2026-08-10T16:58:40.764466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.600995Z","title":"Virtual homogeneity learning: Defending against data heterogeneity in federated learning,","venue":null,"work_id":"0f77e023-a94b-438d-ab00-df16944c8b04","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.769276Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:ea594dcb64d631e47285b0cefba17244f5f2857bcfa4925bcc10db2c484374eb","observation_id":"921c593b-e2f9-46c1-a0d7-355d09021697","resolution":{"observed_at":"2026-08-10T16:58:42.605163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02054","last_updated":"2025-08-24T12:07:07Z","snapshot_observed_at":"2026-08-03T05:02:32.419407Z","submitted_at":"2019-11-05T19:45:49Z","title":"Federated Adversarial Domain Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02054","snapshot_observed_at":"2026-08-10T16:58:40.774331Z","title":"Federated adversarial domain adaptation,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.774331Z"},"links":{"cited_paper":"/paper/1911.02054","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:65ea41b0e07328e7d8ac53d42604b8151540eb001b90cf8f2d019fc37109edcf","observation_id":"63567c5c-dcca-4637-b562-92e7f4bfd6e6","resolution":{"observed_at":"2026-08-10T16:58:40.774331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.585298Z","title":"Collaborative heterogeneous causal inference beyond meta-analysis,","venue":null,"work_id":"2bc2c860-4260-4135-a8bd-7b4ce3fa47e3","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.778532Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:471f6f133f958702a268114ae3f8fb9d85c070d5fe6ef9486b8b272c74207603","observation_id":"71b06c49-a696-4876-b16d-8c304ddd07a4","resolution":{"observed_at":"2026-08-10T16:58:42.591392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04360","last_updated":"2024-08-07T03:36:51Z","snapshot_observed_at":"2026-08-16T14:03:15.831800Z","submitted_at":"2024-04-05T19:14:14Z","title":"Prompt Public Large Language Models to Synthesize Data for Private On-device Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04360","snapshot_observed_at":"2026-08-10T16:58:40.783408Z","title":"Prompt public large language models to synthesize data for private on-device applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.783408Z"},"links":{"cited_paper":"/paper/2404.04360","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:28d6ef57983a4a890ebb056355626085ff34bc2cab957017713ce84d3f112632","observation_id":"6581b927-d0ad-4a98-b1f9-6183bd4a92bb","resolution":{"observed_at":"2026-08-10T16:58:40.783408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.571092Z","title":"Promptmrg: Diagnosis-driven prompts for medical report generation,","venue":null,"work_id":"e5ea624d-33f1-4c0c-93ac-05ed7e3cbad5","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.787756Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:9e296b651eb1e6b434348e8159ab8c5eec3fa13c256c6643818a39b2e7c76b27","observation_id":"40480d00-2c80-474f-887e-aae63efcc52f","resolution":{"observed_at":"2026-08-10T16:58:42.575651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.556636Z","title":"Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,","venue":null,"work_id":"da02f391-a391-49a0-92bf-0bb8ab34eb69","year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.791743Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:bbb24a3447f83cbff72fcadda24d4fad1eb0e469d130b99b7f649932c09592ea","observation_id":"8c24fb7f-81cb-44d8-bffd-0a31ee58d7de","resolution":{"observed_at":"2026-08-10T16:58:42.561465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.470256Z","title":"Sharpness-aware minimization for efficiently improving generalization,","venue":null,"work_id":"b9ace8d5-28ae-4f46-add5-7ddf1d171d78","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.796317Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:1a77826b636e930513e5e1f08780ede5affd44464388762ff09839ba290fccc2","observation_id":"a40cc62b-fe20-41f1-8ec8-382cac11ee8f","resolution":{"observed_at":"2026-08-10T16:58:42.522418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.341293Z","title":"Improving the model consistency of decentralized federated learning,","venue":null,"work_id":"857cee73-f53d-4669-bb9e-8e0a14f2e068","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.800956Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:8f0883765ac72e7949f2c3816e662101a77c6c4fbb4ca9902e3fc605864b9a8d","observation_id":"f7d5f0cd-0458-4689-a09a-d565288e2460","resolution":{"observed_at":"2026-08-10T16:58:42.363874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18890","last_updated":"2024-05-29T08:46:21Z","snapshot_observed_at":"2026-08-16T13:48:17.433525Z","submitted_at":"2024-05-29T08:46:21Z","title":"Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18890","snapshot_observed_at":"2026-08-10T16:58:40.805446Z","title":"Locally estimated global perturba- tions are better than local perturbations for federated sharpness-aware minimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.805446Z"},"links":{"cited_paper":"/paper/2405.18890","citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:d14b062589c0c4ca72de5da8a78261146cbd0a0171a1c7df25f5f04c078509d7","observation_id":"033b4d8c-fee1-40af-9ac6-078df6019d30","resolution":{"observed_at":"2026-08-10T16:58:40.805446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.327786Z","title":"Window-based model averaging improves generalization in heterogeneous federated learning,","venue":null,"work_id":"c4eec637-7b98-4617-894b-9c695e355829","year":2023},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.810003Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:f92d75ca3881d7b9708d5f8e0226d4bfc5cb3f59fae8b8e85dbf73b781e24d93","observation_id":"886d1bdf-80cd-4bf7-9102-d0f11b8a2493","resolution":{"observed_at":"2026-08-10T16:58:42.332058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.313762Z","title":"Swad: Domain generalization by seeking flat minima,","venue":null,"work_id":"ac2cae4c-bb3b-4b46-8c43-3c334e976e8a","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.815123Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:a71db2223155a92ad575bd8d0cff3697374e52f7a19105f7af7524b06e9a5d75","observation_id":"495ce2c7-5a33-4caa-84ae-afda35dd7932","resolution":{"observed_at":"2026-08-10T16:58:42.318792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.298951Z","title":"Fedbn: Federated learning on non-iid features via local batch normalization,","venue":null,"work_id":"b5a9546e-a1d6-4dfb-a0b9-c04146491662","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.819338Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:66850655f9ac98bed00e302265e5c37307ed3de2b2e94886b88303db4223b0ed","observation_id":"acf10424-7bae-4d0a-8b88-7ec7872ae4aa","resolution":{"observed_at":"2026-08-10T16:58:42.303852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.284639Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"c8374e6f-38b6-4f26-ab94-86c16ee2d977","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.824342Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:34cdd2ac9b6694bd5096c78a38b594d099b3516626727c3cbbe23b17a5730b92","observation_id":"0409fab9-cf49-4e3a-af19-80d8139d4824","resolution":{"observed_at":"2026-08-10T16:58:42.288789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.269714Z","title":"Learn from others and be yourself in heterogeneous federated learning,","venue":null,"work_id":"af44b3cd-c433-43c0-8ae4-fad40c57241e","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.867455Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:eedfb2368fe2b912775e3fca17a379b83b285217f579d1cfd2e66cb00ced5227","observation_id":"72b47611-aa58-4d37-8e00-dbe9e95b414e","resolution":{"observed_at":"2026-08-10T16:58:42.274112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.255821Z","title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,","venue":null,"work_id":"2a7ffd56-a5d7-4aca-9a9a-afc1906981e7","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.898608Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:588abb37985e2bc255d3c29f1b371671f6cd0670c15449846d7e2ab879f5bbfa","observation_id":"6ae8a7ee-7921-459f-b5ff-a5a63e1a13a6","resolution":{"observed_at":"2026-08-10T16:58:42.260212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.242120Z","title":"Self-challenging improves cross-domain generalization,","venue":null,"work_id":"1e13df8b-0858-4d9b-8173-9190fa2ef19e","year":2020},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:40.964860Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:b972b3059b98d53303a5a75d743093827383888a7ea00a1a30df83f1ae61740b","observation_id":"544b4430-d17e-48f9-bceb-aceba20b67bc","resolution":{"observed_at":"2026-08-10T16:58:42.246239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.228260Z","title":"Domain generalization with mixstyle,","venue":null,"work_id":"4d362926-ba91-4288-86a0-e46585242d11","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.097245Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:e05cef5ee20be7f51cd9209c3e3146ce92d9b690805c7d8192aeee108d2c6ddf","observation_id":"284d4ba9-93ca-495e-99d7-ecdfb12e8e01","resolution":{"observed_at":"2026-08-10T16:58:42.232266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:42.092130Z","title":"Wilds: A benchmark of in-the-wild distribution shifts,","venue":null,"work_id":"3001ae0c-aae7-479d-bdc6-86785e2cc2eb","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.103267Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:75abcb140395e5734a6e8610207fcbcccd7f4bdcb09731790974b44e7bec8fca","observation_id":"47de2f01-d435-4ac5-9d9d-a02aa14c5188","resolution":{"observed_at":"2026-08-10T16:58:42.141862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.934984Z","title":"Mitosis domain generalization challenge 2022,","venue":null,"work_id":"12733cd0-e1b6-4a3e-94e5-1a1cdcbbd897","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.107294Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:52dc4d7e6163ded9fc5caf53d8c25b554e405c11837adf8a51be3198815582f6","observation_id":"ed58e209-3e0a-4af7-ac02-2cee846eb298","resolution":{"observed_at":"2026-08-10T16:58:41.939969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.921269Z","title":"Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,","venue":null,"work_id":"4ca90d1f-8931-4af0-838c-3e89e58d344d","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.112043Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:608b086f3c0e2ea8627a8c6ec54077ad714eb1365222e78d7b6ccd406b7b8afa","observation_id":"b667fc4e-c1bb-420c-865d-5479330b9014","resolution":{"observed_at":"2026-08-10T16:58:41.926096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.906574Z","title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,","venue":null,"work_id":"ff4a3ecb-49fe-489e-abbe-3ea31171beed","year":2018},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.116437Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:78c374d59024d6f8c8d63df11ca49bbcdf833882fe08545a93e3fd580b9b3f1e","observation_id":"dd4ce40e-2de0-4de7-8074-8f0b943940b8","resolution":{"observed_at":"2026-08-10T16:58:41.911507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.892090Z","title":null,"venue":null,"work_id":"3612ebff-4363-4c74-9610-b8d2515a8f1a","year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.121154Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:0d7ea079dc17ab0cd548d00060f63ec5e682e8d9b645474d44677d98ee8c2810","observation_id":"0625fe0b-8d61-4d1e-8f0b-1fe6f0bec948","resolution":{"observed_at":"2026-08-10T16:58:41.896469Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.876474Z","title":"Bcn20000: Dermoscopic lesions in the wild,","venue":null,"work_id":"1d073c1f-2193-4a19-a0e2-30114d181d0e","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.125995Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:1f6245744fcd7654e82ac359ab1e7147339d71808f50ac08aea7320bc52aecef","observation_id":"96aeda2b-ed5a-4b68-b138-79e5c3a3c4bd","resolution":{"observed_at":"2026-08-10T16:58:41.881815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.761561Z","title":"Loss surfaces, mode connectivity, and fast ensembling of dnns,","venue":null,"work_id":"3e67469e-3056-4d2e-ba84-f8c268697ef8","year":2018},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.131099Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:258ee17f1ac17463b5d67f7e34c742e6db15a6bbcac3b4947eee8593813a0bde","observation_id":"8b7540fa-ba26-49b8-a309-b875abfe2399","resolution":{"observed_at":"2026-08-10T16:58:41.835672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.744183Z","title":"Improving generalization in federated learning by seeking flat minima,","venue":null,"work_id":"313168ba-d544-4f3e-bfc1-4e135e129154","year":2022},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.135649Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:c652d81c75da772c448f3b38ab367f9a9e0250f1d4503c37172d035a385a69b4","observation_id":"781fb7fe-fc92-435c-9dc3-de3006905e4d","resolution":{"observed_at":"2026-08-10T16:58:41.749553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.729678Z","title":"Pain-fl: Personalized privacy-preserving incentive for federated learning,","venue":null,"work_id":"6599c84a-803f-4c6e-b9a5-8c5d1ee534d6","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.140260Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:765c351a0b35ef8fb07490ed1e966d43ee166d14883ac2d09f1f99062b524fa4","observation_id":"3af94070-0873-4691-961b-9d789dabb1fb","resolution":{"observed_at":"2026-08-10T16:58:41.734605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.714644Z","title":"Micronet: Improving image recognition with extremely low flops,","venue":null,"work_id":"762ef6ff-a1a2-4b5c-8234-5e22b54ebf29","year":2021},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.144734Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:98ff26a45be04c32172a41b4309fc4820a188bf2022318b7733e79ca9098ee47","observation_id":"869624fd-d1ae-409b-8de3-e071a4b18922","resolution":{"observed_at":"2026-08-10T16:58:41.719648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.699651Z","title":"Fedgcn: Convergence- communication tradeoffs in federated training of graph convolutional networks,","venue":null,"work_id":"b7391f84-46ee-4a44-b909-67dca111cba7","year":2024},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.149327Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:277bedee2116285854029a622d50d74276a501fe5992de8b594e54720795eb6d","observation_id":"aaacd507-5438-489c-b4d9-7ae9be41dcc2","resolution":{"observed_at":"2026-08-10T16:58:41.704265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.615945Z","title":"Deeper, broader and artier domain generalization,","venue":null,"work_id":"9aaa69bd-2fa5-45dd-871b-3faac5f1f730","year":2017},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.153175Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:7463846ff3f5f56ed01e32e9adea2ad700208f1a741d414377d322c8fccd81da","observation_id":"0933ea69-5a56-4e2f-8cf0-53c2b870d235","resolution":{"observed_at":"2026-08-10T16:58:41.688611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:58:41.157677Z","title":"Image style transfer using convolutional neural networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T16:58:41.157677Z"},"links":{"citing_paper":"/paper/2501.13967"},"observation_digest":"sha256:e93ecef0063395bc40d912f87e2f6972cc3d0fab911e30223ece6e0f45b38146","observation_id":"cc8ec677-4b67-45f2-b547-0ba107fde20b","resolution":{"observed_at":"2026-08-10T16:58:41.157677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.13967","last_updated":"2025-01-27T07:48:49Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T03:03:13.455129Z","submitted_at":"2025-01-22T07:08:45Z","title":"FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":3,"verified_fuzzy":62},"total_outbound_references":83},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 1 inbound Pith citation observation for arXiv:2501.13967."}