{"as_of":"2026-08-19T20:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:945f4f2981fe95518c96e8627ca6d1fd973bddd9c820646c44cce20c57082309","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T11:15:59.653559Z","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":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.02222/citation-record","integrity":"/paper/2608.02222/integrity","json":"/paper/2608.02222/citation-record.json","paper":"/paper/2608.02222"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T11:15:55.015215Z","title":"A survey on federated learning: challenges and applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.015215Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:81995af4e56a346480db3a7abb866c9ef74ed5fd17669d438a66758a553f0d5e","observation_id":"da0c4fae-dd08-482a-add0-b93286ba37e3","resolution":{"observed_at":"2026-08-04T11:15:55.015215Z","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":"2026-08-04T11:15:55.255477Z","title":"Fedbens: One-shot federated learning based on bayesian ensemble,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.255477Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:f70e25065b35ddeaaac43670186c85a4dbac61375f6fbc90d3d2ed5750836d34","observation_id":"9eeebca2-58b9-47b7-9b98-9f7e0f19bc74","resolution":{"observed_at":"2026-08-04T11:15:55.255477Z","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":"2026-08-04T11:15:55.411920Z","title":"Fedtmos: Efficient one-shot federated learning with tsetlin machine,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.411920Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:564030477ff3f7fdede1a17deabca97fb002b0e25393a747aefc41179052e663","observation_id":"ccc6fc26-d22c-4aad-974b-d1848eeab6b0","resolution":{"observed_at":"2026-08-04T11:15:55.411920Z","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":"2026-08-04T11:15:55.514773Z","title":"Revisiting ensembling in one-shot federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.514773Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:b09b35dd3f388f9390e0f3892f7c37deb1a45122128050b84c054cdcec561b4e","observation_id":"24524b7b-7573-47f8-8354-6717bea9f09e","resolution":{"observed_at":"2026-08-04T11:15:55.514773Z","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":"2026-08-04T11:15:55.705102Z","title":"One-shot feder- ated learning: theoretical limits and algorithms to achieve them,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.705102Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:d473e2e7df434273c7109662f9401f3320826e577db3612f208be0583440c98b","observation_id":"66767b12-a315-4d9a-a2ee-15120dfaae92","resolution":{"observed_at":"2026-08-04T11:15:55.705102Z","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":"2026-08-04T11:15:55.919439Z","title":"Dense: Data-free one-shot federated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:55.919439Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:a7658ef81ee72bf14ce7b5a2b4bcf3797ef5a9bde24397b473c89a36d20d7617","observation_id":"a4e2ff22-b72f-4694-b3e2-f474ef8ffba6","resolution":{"observed_at":"2026-08-04T11:15:55.919439Z","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":"2026-08-04T11:15:56.019273Z","title":"Data-free one-shot federated learning under very high statistical heterogeneity,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.019273Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:166ddbe4cba8ef1af8866b89a32aa8c4219a4d3914d0ccd01f05cf0e2a9777e6","observation_id":"90209466-b5e5-44d9-a3c6-1b574531fdf2","resolution":{"observed_at":"2026-08-04T11:15:56.019273Z","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":"2026-08-04T11:15:56.164766Z","title":"Capture global feature statistics for one-shot federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.164766Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:c8fc6f93817f0fd872e70398a34b491b8b4d459b6946337604869856aec9b714","observation_id":"f6a1ece1-555f-4393-9b99-18b25c807fd7","resolution":{"observed_at":"2026-08-04T11:15:56.164766Z","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":"2026-08-04T11:15:56.277110Z","title":"Federated learning via decentralized dataset distillation in resource-constrained edge environments,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.277110Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:adeeed84f08a942cb07a732a986fefefc124ce5b3d1a743023ae97c95b5a3232","observation_id":"567adf25-76aa-4e6a-b296-f734861e91cf","resolution":{"observed_at":"2026-08-04T11:15:56.277110Z","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":"2026-08-04T11:15:56.389546Z","title":"Enhancing one- shot federated learning through data and ensemble co-boosting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.389546Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:4dc8c620107ff514649cbc15b797aa4eade032484df9058cd0c7c041a140c02d","observation_id":"34ebd90c-556f-447a-a121-ea91718d44ca","resolution":{"observed_at":"2026-08-04T11:15:56.389546Z","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":"2026-08-04T11:15:56.530353Z","title":"Federated oriented learning: A practical one-shot personalized federated learning framework,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.530353Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:636cc743ada960e2cad79d4438ead79b11b5751951add3b26bdb5bf792d5839b","observation_id":"873032e0-742e-492d-aab2-8cd603db3422","resolution":{"observed_at":"2026-08-04T11:15:56.530353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04810","last_updated":"2025-03-02T17:18:04Z","snapshot_observed_at":"2026-08-16T13:12:01.623639Z","submitted_at":"2024-10-07T07:45:18Z","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04810","snapshot_observed_at":"2026-08-04T11:15:56.609685Z","title":"Fedbip: Heterogeneous one-shot federated learning with personalized latent diffusion models,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.609685Z"},"links":{"cited_paper":"/paper/2410.04810","citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:7f9e023172cf1aa58c2e4d8e168001bc47ed9c53e588787bb1a3abb0512fece3","observation_id":"6a1f4dc5-7261-4c1e-8803-f70a21fdf048","resolution":{"observed_at":"2026-08-04T11:15:56.609685Z","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":"2026-08-04T11:15:56.871643Z","title":"Feddeo: Description-enhanced one-shot federated learning with diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.871643Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:7930fb7b734ea62a745c36b82a209ac26f564fbf010f349e82e6ed79cfde801a","observation_id":"3045335e-a972-4c5f-bffd-15a9283c700d","resolution":{"observed_at":"2026-08-04T11:15:56.871643Z","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":"2026-08-04T11:15:57.038895Z","title":"Federated generative learning with foundation models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.038895Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:b30787aa2e2a0d6a64c85d192e39e03c7f2cc1f702d29d9cd80cfcfc1085ada3","observation_id":"82e67bf7-4820-4ed0-8d0a-b08a667e352e","resolution":{"observed_at":"2026-08-04T11:15:57.038895Z","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":"2026-08-04T11:15:57.162065Z","title":"Fedlpa: One- shot federated learning with layer-wise posterior aggregation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.162065Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:0233d2b98ce567ab1ffe7597f1a63291d71fe3a73e2f347283ff35cc49464b64","observation_id":"4d49f711-b280-41e8-b05e-2ba4d2bdf326","resolution":{"observed_at":"2026-08-04T11:15:57.162065Z","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":"2026-08-04T11:15:57.335186Z","title":"Fedfisher: Leveraging fisher information for one-shot federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.335186Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:17cb9ad483386f54b8a5d63c94efd539329d9bcbe9e2d3f0bc02708244f1b785","observation_id":"558e2a62-3e7c-4020-b7e2-da2135d3b647","resolution":{"observed_at":"2026-08-04T11:15:57.335186Z","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":"2026-08-04T11:15:57.510451Z","title":"Fusefl: One-shot federated learning through the lens of causality with progressive model fusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.510451Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:21b7a4127874bbda4cdad6c3d3893cd5e269aece44ae5c6191d3d2574e7e4f2e","observation_id":"09a329ca-334b-49dc-8da9-d293463e619d","resolution":{"observed_at":"2026-08-04T11:15:57.510451Z","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":"2026-08-04T11:15:57.659528Z","title":"Does one-shot give the best shot? mitigating model inconsistency in one-shot federated learning,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.659528Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:f425fd3713370816c09d99a86cbd82b0b32fbf09a6191890f33ba8f04d36a485","observation_id":"ef6a4e6d-8427-4c96-8988-c91ae0e9438c","resolution":{"observed_at":"2026-08-04T11:15:57.659528Z","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":"2026-08-04T11:15:57.734305Z","title":"A frequency-based approach for federated domain generalization in heterogeneous medical imaging,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.734305Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:d2ec20b40728ae74e6bff2856958c6405217ae18d4e4090e23a6ce20c700b090","observation_id":"63e3c9fb-028f-4356-a8ef-f825989e4823","resolution":{"observed_at":"2026-08-04T11:15:57.734305Z","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":"2026-08-04T11:15:57.797814Z","title":"Fed2: Feature-aligned federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.797814Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:83d6815b4dbc8e30eb07f6af496ea5a23007606672f0b1f0736f53b408abdeb4","observation_id":"348b92ad-3eaf-4955-8823-cf472bceaf6c","resolution":{"observed_at":"2026-08-04T11:15:57.797814Z","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":"2026-08-04T11:15:57.855060Z","title":"Moment matching for multi-source domain adaptation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.855060Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:9523244aebcb8e1d054b89e73a1e64e73ac0917a8e0d152b2fdff01d4a1464e5","observation_id":"ef133833-3f8a-44be-8ee1-d7c1c67f12fe","resolution":{"observed_at":"2026-08-04T11:15:57.855060Z","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":"2026-08-04T11:15:57.938311Z","title":"pfedafm: Adaptive feature mixture for batch-level personalization in heterogeneous federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.938311Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:85ef0721c34a8225105a6ad88a0179a8b84811bc6b87dd74af6f4835bd3cc3d6","observation_id":"d44a7d22-039f-43d0-a6a6-288612a0787c","resolution":{"observed_at":"2026-08-04T11:15:57.938311Z","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":"2026-08-04T11:15:57.987948Z","title":"Fedrda: Representation deviation alignment in heterogeneous federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:57.987948Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:e25bc40198c78d3ee6baa9526a8facc9670b4ae8ba59a2dd581270c4d52d1147","observation_id":"0a4fd19a-5f87-4b81-95b1-0584cc680b74","resolution":{"observed_at":"2026-08-04T11:15:57.987948Z","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":"2026-08-04T11:15:58.046209Z","title":"One-shot federated learning via synthetic distiller-distillate communication,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.046209Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:fb46180c18540ea40fec5358a850b49fd71f9dc39290e94aa59cef888709ff6a","observation_id":"50c120d4-0a05-4128-a325-be4e5915bd4b","resolution":{"observed_at":"2026-08-04T11:15:58.046209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.07999","last_updated":"2021-06-06T06:55:46Z","snapshot_observed_at":"2026-08-13T09:34:55.152682Z","submitted_at":"2020-09-17T01:14:47Z","title":"Distilled One-Shot Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07999","snapshot_observed_at":"2026-08-04T11:15:58.071592Z","title":"Distilled one-shot federated learning,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.071592Z"},"links":{"cited_paper":"/paper/2009.07999","citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:682df7be36069e6eee94edd1eea704838c5af906becd772be600aaf56acdaa71","observation_id":"95226423-3818-4824-aa17-c3c604ac2b61","resolution":{"observed_at":"2026-08-04T11:15:58.071592Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05148","last_updated":"2020-06-09T09:43:41Z","snapshot_observed_at":"2026-08-09T03:24:41.923040Z","submitted_at":"2020-06-09T09:43:41Z","title":"XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05148","snapshot_observed_at":"2026-08-04T11:15:58.147760Z","title":"Xor mixup: Privacy-preserving data augmentation for one-shot federated learning,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.147760Z"},"links":{"cited_paper":"/paper/2006.05148","citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:2d3a74b3dbb9fba5a09ca3c5bd8476a21d7ab0b2693af71692b9f41a646a6b50","observation_id":"768f38ba-692b-4b0e-9dbd-6a2a1676342c","resolution":{"observed_at":"2026-08-04T11:15:58.147760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01862","last_updated":"2024-02-02T19:34:46Z","snapshot_observed_at":"2026-08-16T14:22:01.237731Z","submitted_at":"2024-02-02T19:34:46Z","title":"Parametric Feature Transfer: One-shot Federated Learning with Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01862","snapshot_observed_at":"2026-08-04T11:15:58.206660Z","title":"Parametric feature transfer: One-shot federated learning with foundation models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.206660Z"},"links":{"cited_paper":"/paper/2402.01862","citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:8413ef2bd5258a9c3066c4a278e7194606d84c6958f08cdb52b76c230af7bb98","observation_id":"4c96c87e-8fa2-46e9-9a8a-78df8b31e24e","resolution":{"observed_at":"2026-08-04T11:15:58.206660Z","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":"2026-08-04T11:15:58.283693Z","title":"Osgan: One-shot distributed learning using generative adversarial networks: A. kasturi, c. hota,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.283693Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:9a0f92dbb63596c0246d2a8ba9d66bf965f9b73b18a7d0c8db5fe5ab924b9522","observation_id":"61735d4e-4457-4c9a-968f-179d798e879f","resolution":{"observed_at":"2026-08-04T11:15:58.283693Z","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":"2026-08-04T11:15:58.405113Z","title":"Clip-guided federated learning on heterogeneity and long-tailed data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.405113Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:ae7a7a06a767c0a9f2b95f5fe043af6ef976cc65d671c736a18f1fac4dbbbeb4","observation_id":"826a993b-0edf-41f9-870b-fd1472d05027","resolution":{"observed_at":"2026-08-04T11:15:58.405113Z","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":"2026-08-04T11:15:58.514588Z","title":"Gpfl: Simultaneously learning global and personalized feature information for personalized federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.514588Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:976eb7bf8e9e818c165cf46513e002c83b56973e2944f146042fa14927895234","observation_id":"40f1ece3-f9e7-4e2e-965a-c4b532b73dea","resolution":{"observed_at":"2026-08-04T11:15:58.514588Z","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":"2026-08-04T11:15:58.635800Z","title":"Fraug: Tackling federated learning with non-iid features via representation augmenta- tion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.635800Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:eedc8917b86b7422c6e9cbac6d229637b719e10d78842b6bc0a7fa0f924bc812","observation_id":"08eff3bc-30fb-4c0f-91d5-ed41b9350f23","resolution":{"observed_at":"2026-08-04T11:15:58.635800Z","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":"2026-08-04T11:15:58.753658Z","title":"Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.753658Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:36e1c9f679312cb6d9c1c294cae82023d1763b8e48dfba48a1331a4a7fc3b39d","observation_id":"71125373-ece4-4bab-8635-769dc6756796","resolution":{"observed_at":"2026-08-04T11:15:58.753658Z","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":"2026-08-04T11:15:58.879555Z","title":"Fedfm: Anchor- based feature matching for data heterogeneity in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:58.879555Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:79b3b1d4fc200dbdedea796f2dfa645c014c2d81d0bdf2309ad542878b6d6b98","observation_id":"8fb8eddb-1858-4c64-9eee-858ec21739c8","resolution":{"observed_at":"2026-08-04T11:15:58.879555Z","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":"2026-08-04T11:15:59.033991Z","title":"Fedfed: Feature distillation against data heterogeneity in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.033991Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:36368d6a2d8330d3e4b32df633f02203deaee3bab2c849c956cd72fe03ecbcb4","observation_id":"0b2ff936-b2a2-483a-b04b-cc5923740c24","resolution":{"observed_at":"2026-08-04T11:15:59.033991Z","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":"2026-08-04T11:15:59.169553Z","title":"Similarity of neural network representations revisited,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.169553Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:9e9d7e2929b8bddcaf85eddc5d4e58707b25b7115662606bac3af0c3f49effcc","observation_id":"0b4fabc5-3ec1-43c5-ab04-fdecb762f162","resolution":{"observed_at":"2026-08-04T11:15:59.169553Z","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":"2026-08-04T11:15:59.267308Z","title":"Deep domain- adversarial image generation for domain generalisation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.267308Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:b946e696560e19a5bd768591325ff20b23c1a9765f4e9cd756fe1348ef7215e8","observation_id":"2dfaa241-ccc9-4643-b902-14aebede021e","resolution":{"observed_at":"2026-08-04T11:15:59.267308Z","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":"2026-08-04T11:15:59.405231Z","title":"Deep hashing network for unsupervised domain adaptation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.405231Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:d269978a443f82ec591e465cae98f195e1fe56a390b937c36a0a8002c5670861","observation_id":"d5688c15-3b37-4d83-b8f5-c384a2f8a8d2","resolution":{"observed_at":"2026-08-04T11:15:59.405231Z","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":"2026-08-04T11:15:59.509177Z","title":"Fed- erated discriminative representation learning for image classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.509177Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:5b4ee9c63fc617851fc9483d93cacb5c036c50e24faa373876cb36b90ba8e8e0","observation_id":"02346201-77fb-4aa2-b51a-9c38954240f4","resolution":{"observed_at":"2026-08-04T11:15:59.509177Z","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":"2026-08-04T11:15:59.653559Z","title":"Vector quantization-based clustered federated learning with global feature anchors for improved representation and generalization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:59.653559Z"},"links":{"citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:a600e239fd2667f613958b5d002ba32de1b050f5a95161b1d4c481a9d488c0d8","observation_id":"c0b3364f-ec30-4d81-8c44-aa4508fc676b","resolution":{"observed_at":"2026-08-04T11:15:59.653559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04810","last_updated":"2025-03-02T17:18:04Z","snapshot_observed_at":"2026-08-16T13:12:01.623639Z","submitted_at":"2024-10-07T07:45:18Z","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":"2410.04810","doi":"10.48550/arxiv.2410.04810","metadata_source":"pith","pith_arxiv_id":"2410.04810","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","venue":"cs.LG","work_id":"e030a3b8-d9e9-4faa-b89c-b4bf0affab8d","year":2024},"citing_paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:56.713106Z"},"links":{"cited_paper":"/paper/2410.04810","citing_paper":"/paper/2608.02222"},"observation_digest":"sha256:2f691c89def30aaeb1aeece42f41ca710358634457962b4aba2a362f87477e56","observation_id":"e7426926-49ae-4c2d-8d69-78e4b1ed1192","resolution":{"observed_at":"2026-08-04T11:18:38.187672Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.02222","last_updated":"2026-08-03T13:41:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T22:05:43.006587Z","submitted_at":"2026-08-03T13:41:07Z","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":39,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":40},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.02222."}