{"as_of":"2026-08-15T13:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:891eb0565cb269ec476d1a9acfc46da7302187d32cc2e1a5b5ae52eacc210250","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:18:19.586301Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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":"2026-05-08T18:44:45.397374Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-09T06:15:37.500328Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"cited_work":{"arxiv_id":"2501.15486","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.15486","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fedalign: Federated domain general- ization with cross-client feature alignment","venue":null,"work_id":"68269a96-52da-4d1e-ae24-be35c65c3dbb","year":2025},"citing_paper":{"arxiv_id":"2605.04108","last_updated":"2026-05-04T16:43:22Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-05-04T16:43:22Z","title":"MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T18:44:45.397374Z"},"links":{"cited_paper":"/paper/2501.15486","citing_paper":"/paper/2605.04108"},"observation_digest":"sha256:773fa3cccaa3bf22a00e55f2fb62aadc872763f03bc1c1c4d55a14c58d266533","observation_id":"307be597-10e5-4dbc-b7d2-8c60977aace2","resolution":{"observed_at":"2026-05-09T06:15:37.507731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.15486/citation-record","integrity":"/paper/2501.15486/integrity","json":"/paper/2501.15486/citation-record.json","paper":"/paper/2501.15486"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:18:18.751041Z","title":"Wasserstein generative adversarial networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.751041Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:eb5e0f7b9ead956001311aa51a7b3a7d7359a5d8e39e92b80db065f0c94a2da5","observation_id":"fae8eb2a-4000-443c-bb6d-712d3898fd37","resolution":{"observed_at":"2026-08-10T14:18:18.751041Z","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":"2026-08-10T14:18:20.989680Z","title":"Federated domain generalization for image recognition via cross-client style transfer","venue":null,"work_id":"6ac28d7d-f340-43de-bd75-972cafc90f8d","year":2023},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.801549Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:70b60da45d1ca7f80370e3add6bb7da671f4def76b51c78174e550baba2c6522","observation_id":"ee8411d7-71dd-4a0d-8352-feaaa643fb45","resolution":{"observed_at":"2026-08-10T14:18:20.993468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.955117Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"fd39f28c-761c-49d7-a254-23e8a199ac5d","year":2015},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.841160Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:c50ae9b5ef25af2b3d4016d98d076883d60f610848a83395e0fb39b9897e25b5","observation_id":"959633a3-e7fa-42aa-afc6-07226b09e529","resolution":{"observed_at":"2026-08-10T14:18:20.981518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.887352Z","title":"Domain-adversarial training of neural networks","venue":null,"work_id":"49bafb5e-aaa0-4bc3-9c89-bf3cfb476c5e","year":2016},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.892487Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:915f48828e9d7dd2df4372a93bba78cfa6053204a9db3cdc7cd6019f1daf06d1","observation_id":"e46dd1fb-9695-4aab-bba2-595856cf9c27","resolution":{"observed_at":"2026-08-10T14:18:20.922910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.774311Z","title":"Dlow: Domain flow for adaptation and generalization","venue":null,"work_id":"4c66c59d-c296-409f-ad11-d86234934c35","year":2019},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.945488Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:dabc12b1fedce531a5670807261648715f6521a2ab4940e32678c891e5f68551","observation_id":"3d08a52b-350f-44de-9c76-dcd2108832e2","resolution":{"observed_at":"2026-08-10T14:18:20.808249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.744795Z","title":"Caltech-256 object category dataset","venue":null,"work_id":"855230c6-8c2e-4552-aaa2-4b7b8266bf8c","year":2007},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.965853Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:dd6ba3745f7789f1861c5f5828525e83cdac48719f5d7e7da4bb5cb1a2c454a5","observation_id":"af3233ec-6233-474e-a894-ac89e79d84a2","resolution":{"observed_at":"2026-08-10T14:18:20.748319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.733875Z","title":"Out-of-distribution generalization of federated learning via implicit invariant relationships","venue":null,"work_id":"81247966-a4f7-4ad5-bd81-2733f1d90d29","year":2023},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.970515Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:bac76c38e681fcd75088170cb6ce8630b2a57d30c9585ffb0c99cb9baf0e4475","observation_id":"bb7908c7-d89f-456e-9275-8132f31f7886","resolution":{"observed_at":"2026-08-10T14:18:20.738165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.721567Z","title":"Arbitrary style transfer in real-time with adaptive instance normalization","venue":null,"work_id":"8537dd3b-aece-4924-a687-3a222571f77b","year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.978439Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:1502d28dccb22bae939bd2c1429a38d216b950392273da0b4188a94415907726","observation_id":"73fd53e3-5d8a-4f10-88a1-3782011345d8","resolution":{"observed_at":"2026-08-10T14:18:20.725354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.711189Z","title":"Deeper, broader and artier domain generalization","venue":null,"work_id":"07ddb259-1bfa-4db2-b8b0-9de52753326c","year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.986285Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:62b887dd806db3bbc69afac6a958881ed91afdf5842f6343294f9eff0447c92f","observation_id":"f9538a61-1740-4927-9386-cbd282e5cb19","resolution":{"observed_at":"2026-08-10T14:18:20.714999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.699875Z","title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","venue":null,"work_id":"393a8b36-4ead-489f-ad81-0262aaf801db","year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:18.996417Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:184497a2a05ce857ddeef150f15434eb5c986b88fc72245d9a7ab82545f268c8","observation_id":"7c096b8b-9e89-4bc3-87ed-2ae1907d3ef1","resolution":{"observed_at":"2026-08-10T14:18:20.703862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.003553Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.003553Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:8965b526dc0686c982b19675e32aa72a81b04bd024de6f05442af0ea9da2b26c","observation_id":"b12f2507-d35b-48bb-8829-9a50989a3318","resolution":{"observed_at":"2026-08-10T14:18:19.003553Z","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":"2026-08-10T14:18:20.682495Z","title":"Zero-shot knowledge transfer via adversarial belief matching","venue":null,"work_id":"5c1fad37-a3f2-46df-9978-80857f12ac66","year":2019},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.008262Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:c8dd533fdd5951f0cd9fec31f551c9ebd6952e8396bf48eefe3415a18c8b90df","observation_id":"0076a503-ffef-493e-9602-05337c64fccb","resolution":{"observed_at":"2026-08-10T14:18:20.685857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.644104Z","title":"A survey on security and privacy of federated learning","venue":null,"work_id":"3993cd4c-917e-4107-9e15-dd445dfaad08","year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.011305Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:f9cb1d4c266d8a62a96c39eed9dc71d93848f3308d6b669fbc4182f07647f4f0","observation_id":"bbaa9235-c078-4bc2-932b-e7c3a96c9ba5","resolution":{"observed_at":"2026-08-10T14:18:20.675278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.591493Z","title":"Fedsr: A simple and effective domain generalization method for federated learning","venue":null,"work_id":"e7cc3f0d-003d-466c-8953-21e544603375","year":2022},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.014865Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:e3458dd24b0c25d09507ed4bb4a6938ca17fd3f18e818f6c6b1b1e196f9a3634","observation_id":"b96fa159-9d18-4b58-9843-248de8ce9267","resolution":{"observed_at":"2026-08-10T14:18:20.609440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.508138Z","title":"Domain generalization with interpolation robustness","venue":null,"work_id":"219a3960-cee3-49db-a24a-0c48da35afd5","year":2024},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.023853Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:60c978afa08e622c29f43e4fb8d42acf8e0a5ff4bf18770490ef54f2fcc7ea39","observation_id":"08c3336b-422e-49ca-aaf6-c03cde2f7b95","resolution":{"observed_at":"2026-08-10T14:18:20.538513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.356315Z","title":"Stablefdg: style and attention based learning for federated domain generalization","venue":null,"work_id":"9bc8fa65-4bcc-4564-b973-1d18d8d8f0cf","year":2024},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.029174Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:cc9a6c144e2796fdbb407a819d619259cf63da436539e1a65dfc90b5bf03de44","observation_id":"f6459bcf-7218-45e3-81ee-377d6fedb0db","resolution":{"observed_at":"2026-08-10T14:18:20.456945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.032926Z","title":"Federated adversarial domain adaptation","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.032926Z"},"links":{"cited_paper":"/paper/1911.02054","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:463f8743bf5582281a8b05034179e1452a6d5bad5167527093314ba53df45af1","observation_id":"f51a9878-60b6-464e-ba12-d0acd06c8b03","resolution":{"observed_at":"2026-08-10T14:18:19.032926Z","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":"2026-08-10T14:18:19.041553Z","title":"Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389--5400","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.041553Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:765cea2b2b7b07e5efb9fcf5b9395676863cb0e9c5fdd53206b2bb6d9273189f","observation_id":"ca6e8bda-2780-4af6-a5ec-7ac0f9f52f24","resolution":{"observed_at":"2026-08-10T14:18:19.041553Z","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":"2026-08-10T14:18:20.251565Z","title":"Model-based domain generalization","venue":null,"work_id":"b4b53188-2d5e-4655-8aaa-f5e5be60aa04","year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.045721Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:72a7e220a96b88e0b43946f4dc9f4e82d6ecb8fa5c0e330b25d2222a187c2f27","observation_id":"feaa8f11-9446-4f60-a0d2-6bc59b4e2e37","resolution":{"observed_at":"2026-08-10T14:18:20.277742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-10T14:18:19.049305Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.049305Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:6774c6379bb7ea2a9cb3879cee921c6c82b7f00c20175cdbe2089affc6b7f691","observation_id":"ce5da9b9-0e4b-46d8-9c9e-cbd1ad7cd5a0","resolution":{"observed_at":"2026-08-10T14:18:19.049305Z","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":"2026-08-10T14:18:20.240535Z","title":"Deep coral: Correlation alignment for deep domain adaptation","venue":null,"work_id":"29e3c1cb-8017-4ba0-94be-01b22bf84c37","year":2016},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.053540Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:690016a842143fa30d5fb7f331bf99276f7053155543da17022e0e9e5b6f90be","observation_id":"aa1fca8a-63ec-472d-88a9-e9cf73259da1","resolution":{"observed_at":"2026-08-10T14:18:20.244742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.3474","last_updated":"2014-12-10T21:20:54Z","snapshot_observed_at":"2026-08-14T23:08:06.157493Z","submitted_at":"2014-12-10T21:20:54Z","title":"Deep Domain Confusion: Maximizing for Domain Invariance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.3474","snapshot_observed_at":"2026-08-10T14:18:19.057261Z","title":"Deep domain confusion: Maximizing for domain invariance","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.057261Z"},"links":{"cited_paper":"/paper/1412.3474","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:4a7a67bd1876440ca1ad618635553ce2d7637a4306fc39f01dc319b92eca388a","observation_id":"ead98231-0e2c-4f98-912e-e53e093113ed","resolution":{"observed_at":"2026-08-10T14:18:19.057261Z","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":"2026-08-10T14:18:20.228805Z","title":"Deep hashing network for unsupervised domain adaptation","venue":null,"work_id":"932bb52e-ff4e-44e6-8315-3ef91e1c9adb","year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.073803Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:aafb468486e100b7166fa907eab73be207a124bbdd50223ccd19bb5b6d177807","observation_id":"9f0922e1-157c-42ae-953d-069e40b510f2","resolution":{"observed_at":"2026-08-10T14:18:20.233116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.217578Z","title":"Addressing model vulnerability to distributional shifts over image transformation sets","venue":null,"work_id":"cb14b203-1f2c-4862-85fc-efe7f82bf491","year":2019},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.126093Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:7750371dff2a2d279b30b52a7e59de62da1ceeb4cadcfcc1d2100363c8c294bd","observation_id":"781dee70-b173-413f-9552-644efdf52f99","resolution":{"observed_at":"2026-08-10T14:18:20.221848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.207478Z","title":"Generalizing to unseen domains via adversarial data augmentation","venue":null,"work_id":"55f5f4ab-36c3-4df6-b9f2-e8af282e465a","year":2018},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.176956Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:43539a709cf0f456bc0fc5c10eb2ab3551d607233a1cc248ec9891af50a390d8","observation_id":"4d9dd727-7ca1-4c98-ad54-fcb9dd21d2f7","resolution":{"observed_at":"2026-08-10T14:18:20.210809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.197444Z","title":"Visual domain adaptation with manifold embedded distribution alignment","venue":null,"work_id":"497a0494-a19a-46d8-b98c-70c14a4b6155","year":2018},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.232455Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:fb3b1dbe1973080ff2ec48596d6bc615e73ea31595eda74b1d8325d0f23bbf6a","observation_id":"e8d7fe0c-627d-4325-8432-00a914a58555","resolution":{"observed_at":"2026-08-10T14:18:20.200937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.187371Z","title":"Transfer learning with dynamic distribution adaptation","venue":null,"work_id":"e0f82cfa-c52c-4401-95ec-12923bea2537","year":2020},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.278154Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:f70f49c1a8ef497d6ae6de27cff4b57105ea906c5cfe481230964b7c07447396","observation_id":"3fb6eb7e-d0f5-45c8-9191-dc6eeb5b5281","resolution":{"observed_at":"2026-08-10T14:18:20.190623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.13003","last_updated":"2021-05-03T16:12:15Z","snapshot_observed_at":"2026-08-04T12:08:52.633467Z","submitted_at":"2020-07-25T19:52:25Z","title":"Robust and Generalizable Visual Representation Learning via Random Convolutions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.13003","snapshot_observed_at":"2026-08-10T14:18:19.306565Z","title":"Robust and generalizable visual representation learning via random convolutions","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.306565Z"},"links":{"cited_paper":"/paper/2007.13003","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:976b6df06c2e09d8361e0021a5b5b96dba170b81b89dc66fe894d11cd7527581","observation_id":"90fca822-187f-4fd7-95b5-d7c0fb52c19c","resolution":{"observed_at":"2026-08-10T14:18:19.306565Z","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":"2026-08-10T14:18:20.147174Z","title":"Federated adversarial domain hallucination for privacy-preserving domain generalization","venue":null,"work_id":"13ac1d31-1119-42e0-a519-05f35a3c6671","year":2023},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.377813Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:49aef1aa8608a68731b09a2062d8662434dcdd8a6b816b7a4e5ca2efe31404dc","observation_id":"40014585-99ed-44b0-9350-23501dd8896b","resolution":{"observed_at":"2026-08-10T14:18:20.180491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:20.055885Z","title":"Fda: Fourier domain adaptation for semantic segmentation","venue":null,"work_id":"e061bd41-f141-40f1-b300-6c9093ac70e3","year":2020},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.431782Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:f40001fe419fb2ee148f2a8891b6c107eb1483b7b86016534921836ff5606a77","observation_id":"9fcb2197-c56f-48c8-a19b-74c5dab7914e","resolution":{"observed_at":"2026-08-10T14:18:20.097522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.957052Z","title":"Federated multi-target domain adaptation","venue":null,"work_id":"c0f09883-39af-42da-80fe-9618642f93a7","year":2022},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.474928Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:2c9acd0dfbd57aaeab9c180fcc794b41e30f52e3ef507d4f314ad6f219616a5b","observation_id":"e6433bd8-8928-4e06-8b76-445c991b30cc","resolution":{"observed_at":"2026-08-10T14:18:20.006644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00233","last_updated":"2021-07-01T06:14:51Z","snapshot_observed_at":"2026-08-13T18:53:51.701120Z","submitted_at":"2021-07-01T06:14:51Z","title":"FedMix: Approximation of Mixup under Mean Augmented Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.00233","snapshot_observed_at":"2026-08-10T14:18:19.517940Z","title":"Fedmix: Approximation of mixup under mean augmented federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.517940Z"},"links":{"cited_paper":"/paper/2107.00233","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:e880319cfa5afda914551d00bf3568d1ae3d5198cfd5a65a001a8cae7255e257","observation_id":"db7ae9c5-4a94-4112-8ba2-f5a181576200","resolution":{"observed_at":"2026-08-10T14:18:19.517940Z","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":"2026-08-10T14:18:19.844614Z","title":"mixup: Beyond empirical risk minimization","venue":null,"work_id":"22398410-69ae-40fe-b57b-d9368af3b46a","year":2018},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.550769Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:39adef9a708516b6e9d9338f5392e195792d009cc89c4a4e768776b434346d17","observation_id":"0845ca6f-5691-40e7-b4d9-799730f61c5e","resolution":{"observed_at":"2026-08-10T14:18:19.922984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.10487","last_updated":"2023-01-07T04:15:38Z","snapshot_observed_at":"2026-08-14T05:21:55.857642Z","submitted_at":"2021-11-20T01:02:36Z","title":"Federated Learning with Domain Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.10487","snapshot_observed_at":"2026-08-10T14:18:19.557534Z","title":"Federated learning with domain generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.557534Z"},"links":{"cited_paper":"/paper/2111.10487","citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:e515d8b84e2c7ec171efbf6fc3f806c0b602ff56d5be51f12d8760337512612b","observation_id":"b7d4630c-12f5-47f8-b913-6e96ad778d4f","resolution":{"observed_at":"2026-08-10T14:18:19.557534Z","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":"2026-08-10T14:18:19.729842Z","title":"Federated domain generalization with generalization adjustment","venue":null,"work_id":"477c822b-b60b-4a59-b7a0-0860e6f77f38","year":2023},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.561092Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:1669a15448560e454997339564663eb76f9d0143530158b0496ec33911c45365","observation_id":"35726298-8d88-48fd-afd9-6fda88d606f5","resolution":{"observed_at":"2026-08-10T14:18:19.761538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.717780Z","title":"Domain adaptive ensemble learning","venue":null,"work_id":"93945156-12ac-4722-a86e-50640e18d66e","year":2021},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.568014Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:54759bf932f598d51f965c8e64413f69ad3321a3e5f66aaf907bf9dbf76bc085","observation_id":"23cf8459-c376-49af-93a9-5d1bb5a299c2","resolution":{"observed_at":"2026-08-10T14:18:19.722035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.705504Z","title":"Domain generalization: A survey","venue":null,"work_id":"c98c40f5-4d4e-4227-940e-880933c7764f","year":2022},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.571436Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:90f10a2143f0cf82ebbb8c8c1435ce6585b8ac635c56405fa93f3a34c9780d85","observation_id":"a6891bbf-3aa0-460b-aa80-de029dc262ac","resolution":{"observed_at":"2026-08-10T14:18:19.709338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.691279Z","title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","venue":null,"work_id":"038d231c-03c9-4025-8ae8-07eab5d2e14d","year":2017},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.578350Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:769824745acd0e88d029b6454b13efd2b512f73624f1a5afe2ffc777bd47e35b","observation_id":"439aab0b-0b66-42b7-a27d-6bfa1b2b565c","resolution":{"observed_at":"2026-08-10T14:18:19.696984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T14:18:19.586301Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T14:18:19.586301Z"},"links":{"citing_paper":"/paper/2501.15486"},"observation_digest":"sha256:2c2122690c0d4c786ccdf01f5f413c25ea11d12d38a5a88d92ffa071a8dd7264","observation_id":"5213e2f8-f886-4f3f-94d5-bc00495757af","resolution":{"observed_at":"2026-08-10T14:18:19.586301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.15486","last_updated":"2025-01-26T11:17:32Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T22:51:09.904079Z","submitted_at":"2025-01-26T11:17:32Z","title":"FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":29},"total_outbound_references":39},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.15486."}