{"as_of":"2026-08-08T21:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9d91bf9d0edb2081e47770016cf5bf0e89cd612aabd02fe6202e868adb8bf6a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":20,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T23:36:53.758911Z","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":"2026-06-30T16:44:56.408203Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":"2002.10619","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-06-30T16:44:56.408203Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":"e98a59b1-a0f5-4d83-afb4-c201efeddeb8","year":2002},"citing_paper":{"arxiv_id":"2208.01618","last_updated":"2022-08-02T17:50:36Z","snapshot_observed_at":"2026-08-02T23:40:32.342515Z","submitted_at":"2022-08-02T17:50:36Z","title":"An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-11T18:08:55.311069Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2208.01618"},"observation_digest":"sha256:07b00778d4c2157ad917dfdd5dbb5c4e7d3ba5c82adf11d182bf866c811381dc","observation_id":"45f6f7df-f5b2-40d7-bee6-a0bfef6a2165","resolution":{"observed_at":"2026-05-11T18:08:55.582405Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":"2002.10619","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-06-30T16:44:56.408203Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":"e98a59b1-a0f5-4d83-afb4-c201efeddeb8","year":2002},"citing_paper":{"arxiv_id":"2406.10861","last_updated":"2024-06-16T09:12:16Z","snapshot_observed_at":"2026-07-06T18:31:40.910349Z","submitted_at":"2024-06-16T09:12:16Z","title":"Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions","version":1},"reference_index":103,"source":"pdf_text","source_observed_at":"2026-05-23T23:47:28.874336Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2406.10861"},"observation_digest":"sha256:0122d3373a9f38df61f517758fc5a90c3cc028149a42d88be96c80f5f056f04e","observation_id":"c4fbd592-5bde-4ea5-90e1-fcd0fe64db61","resolution":{"observed_at":"2026-05-23T23:48:39.291221Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-07T23:36:53.758911Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.08829","last_updated":"2026-07-19T01:09:13Z","snapshot_observed_at":"2026-08-07T23:30:50.142137Z","submitted_at":"2025-02-12T22:35:29Z","title":"PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.758911Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:2da9be08dfba2b8066e8f6424fafeb080730f35d5af72738c771f78c029f753f","observation_id":"b1256a1d-8066-47f0-8b59-519742a3dce9","resolution":{"observed_at":"2026-08-07T23:36:53.758911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-07T14:57:56.994795Z","title":"Mansour, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16857","last_updated":"2025-05-22T16:15:12Z","snapshot_observed_at":"2026-08-07T18:12:02.052929Z","submitted_at":"2025-05-22T16:15:12Z","title":"Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:57:56.994795Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2505.16857"},"observation_digest":"sha256:1e5e4f0a2447f3cc793729f768f22bd3f755e80531333ffeb450d7d3fca45342","observation_id":"5238e042-0f17-441a-995b-541a5aa9b733","resolution":{"observed_at":"2026-08-07T14:57:56.994795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-07T12:01:28.717182Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.00932","last_updated":"2025-06-01T09:53:54Z","snapshot_observed_at":"2026-08-07T11:52:15.996106Z","submitted_at":"2025-06-01T09:53:54Z","title":"Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T12:01:28.717182Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2506.00932"},"observation_digest":"sha256:668fef655f691c2fd47b10ffdd6c905dfd39b2ff2ad9e049560a0203b8599352","observation_id":"94ff3aa7-7dd1-4983-9dbc-1690f71e98c2","resolution":{"observed_at":"2026-08-07T12:01:28.717182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-07T05:32:23.045514Z","title":"Three approaches for personalization with applications to federated learning.arXiv preprint arXiv:2002.10619,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.07769","last_updated":"2025-06-09T13:46:10Z","snapshot_observed_at":"2026-08-08T16:43:41.553976Z","submitted_at":"2025-06-09T13:46:10Z","title":"Clustered Federated Learning via Embedding Distributions","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:23.045514Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2506.07769"},"observation_digest":"sha256:ed1fbf75125c312e5f646c70cfed7d7b9b7a240a9b3d23f619e172d8d7ee616b","observation_id":"2a897e71-cf47-467d-b647-4cac2bea47e7","resolution":{"observed_at":"2026-08-07T05:32:23.045514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-07T01:03:58.969749Z","title":"Three approaches for person- alization with applications to federated learning.arXiv preprint arXiv:2002.10619, 2020","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.12303","last_updated":"2025-06-14T01:40:31Z","snapshot_observed_at":"2026-08-08T15:59:52.132513Z","submitted_at":"2025-06-14T01:40:31Z","title":"SPIRE: Conditional Personalization for Federated Diffusion Generative Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T01:03:58.969749Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2506.12303"},"observation_digest":"sha256:f78e534404de47973983221f7f60790349b3fccd814b542389e09321a2454cce","observation_id":"e70b6994-4d53-4400-9cb1-497356762b1b","resolution":{"observed_at":"2026-08-07T01:03:58.969749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T19:04:52.825925Z","title":"Mansour, M","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.06844","last_updated":"2025-07-09T13:44:27Z","snapshot_observed_at":"2026-08-08T08:10:46.516992Z","submitted_at":"2025-07-09T13:44:27Z","title":"Adaptive collaboration for online personalized distributed learning with heterogeneous clients","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:04:52.825925Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.06844"},"observation_digest":"sha256:37cfcd28aa891c1bfea705788b168860a5759d64a582dbba3cbf1797f891892b","observation_id":"7400da7d-53ab-4665-9a11-dc494c0ceedf","resolution":{"observed_at":"2026-08-06T19:04:52.825925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T19:00:01.503047Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.06931","last_updated":"2025-07-09T15:13:44Z","snapshot_observed_at":"2026-08-08T11:14:49.477120Z","submitted_at":"2025-07-09T15:13:44Z","title":"DICE: Data Influence Cascade in Decentralized Learning","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-06T19:00:01.503047Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.06931"},"observation_digest":"sha256:bc45209938bba9b0469fba9d3b9bde97e37df53be1d574f2a2e0734dad62f5f5","observation_id":"2250fe74-8c08-45ae-87d6-2b4daef550eb","resolution":{"observed_at":"2026-08-06T19:00:01.503047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T16:42:15.498048Z","title":"Three approaches for per- sonalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.12903","last_updated":"2025-07-17T08:42:48Z","snapshot_observed_at":"2026-08-06T16:33:03.140175Z","submitted_at":"2025-07-17T08:42:48Z","title":"Federated Learning for Commercial Image Sources","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:15.498048Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.12903"},"observation_digest":"sha256:313b194d7db5cabe701c347db42f119de84e902a1b1a3e5216a4f218ad3a87be","observation_id":"790a10df-2419-4f58-b6a4-1af30c781524","resolution":{"observed_at":"2026-08-06T16:42:15.498048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T14:54:36.966964Z","title":"Mansour, M","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.17534","last_updated":"2025-07-23T14:13:19Z","snapshot_observed_at":"2026-08-06T14:44:02.439597Z","submitted_at":"2025-07-23T14:13:19Z","title":"Federated Majorize-Minimization: Beyond Parameter Aggregation","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-06T14:54:36.966964Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.17534"},"observation_digest":"sha256:51217a2a23a4c24db585fa0a72e98fac7669dfd382497c6b4033484964ba2076","observation_id":"54a41fcd-07fd-4ab1-b0e2-54c655f40317","resolution":{"observed_at":"2026-08-06T14:54:36.966964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T11:45:58.871376Z","title":"Three approaches for personalization with applications to federated learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-08T09:43:57.385655Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.871376Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:d0ff49f232d45f80bff0ac485b5e29ed7dc82d46656ff457616c8f36197e71ab","observation_id":"294525c7-8d89-4c6a-b1f8-b641b253d10d","resolution":{"observed_at":"2026-08-06T11:45:58.871376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-05T20:19:07.921328Z","title":"Mansour, M","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2508.10840","last_updated":"2025-08-14T17:06:50Z","snapshot_observed_at":"2026-08-08T10:54:48.141750Z","submitted_at":"2025-08-14T17:06:50Z","title":"Generalizable Federated Learning using Client Adaptive Focal Modulation","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-05T20:19:07.921328Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2508.10840"},"observation_digest":"sha256:10ceacf7f9b698541f3ffdf55c13c7a50c175d5982681cff15f6eb2c3f7bb126","observation_id":"8bbceb6b-6779-4284-9cf3-5b69e68ad720","resolution":{"observed_at":"2026-08-05T20:19:07.921328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-05T12:26:27.982200Z","title":"Mansour, M","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.01587","last_updated":"2025-09-01T16:18:51Z","snapshot_observed_at":"2026-08-05T12:26:26.084448Z","submitted_at":"2025-09-01T16:18:51Z","title":"One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T12:26:27.982200Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2509.01587"},"observation_digest":"sha256:043814de6f381a9ab280dd6f8552bf6db64e7038663ca17c4373f6a4db52b21c","observation_id":"4fd8d3a3-256e-457b-a01c-c344e6e85875","resolution":{"observed_at":"2026-08-05T12:26:27.982200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-04T22:44:40.660528Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.660528Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:ab4b0fef8920f5a05d19a3d6f506ffe453116b2409788c9de96933c7dcce2af6","observation_id":"79e19cdf-18b4-4e19-9fc8-7d7aefc37eee","resolution":{"observed_at":"2026-08-04T22:44:40.660528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-04T09:30:12.884697Z","title":"Othmane Marfoq, Chuan Xu, Giovanni Neglia, and Richard Vidal","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2510.15300","last_updated":"2026-03-02T07:22:42Z","snapshot_observed_at":"2026-08-06T03:46:19.350156Z","submitted_at":"2025-10-17T04:17:00Z","title":"DFCA: Decentralized Federated Clustering Algorithm","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T09:30:12.884697Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2510.15300"},"observation_digest":"sha256:274cf26c089068d2bc5f2f0a013cd623ab5433185636f0e3d2e94622d8551375","observation_id":"b9b3f7d9-f136-4e46-8e19-dcae5df4a4f1","resolution":{"observed_at":"2026-08-04T09:30:12.884697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-03T23:35:19.198405Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.11625","last_updated":"2026-06-19T16:10:00Z","snapshot_observed_at":"2026-08-07T13:01:53.722785Z","submitted_at":"2025-11-07T08:48:03Z","title":"MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T23:35:19.198405Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2511.11625"},"observation_digest":"sha256:e5bfd79e406772ef22c7e61ca09e9fe61534664da54daf7c7abd7da99d7aa1d3","observation_id":"d74f25de-e625-44d1-b8b0-99decf382757","resolution":{"observed_at":"2026-08-03T23:35:19.198405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-03T03:49:39.995547Z","title":null,"venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2602.07218","last_updated":"2026-05-29T20:42:03Z","snapshot_observed_at":"2026-08-07T08:11:04.251717Z","submitted_at":"2026-02-06T21:59:40Z","title":"Collaborative and Efficient Fine-tuning: Leveraging Task Similarity","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T03:49:39.995547Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2602.07218"},"observation_digest":"sha256:857ca8b90d9f2b52a5dbc728f7affeb1eb3414f88d54ace2928efb6418eb13b8","observation_id":"bc5e11c3-637d-42f6-aebd-068a076cf9c7","resolution":{"observed_at":"2026-08-03T03:49:39.995547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":"2002.10619","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-06-30T16:44:56.408203Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":"e98a59b1-a0f5-4d83-afb4-c201efeddeb8","year":2002},"citing_paper":{"arxiv_id":"2604.10678","last_updated":"2026-04-12T15:13:41Z","snapshot_observed_at":"2026-08-02T21:21:33.785507Z","submitted_at":"2026-04-12T15:13:41Z","title":"FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T15:17:38.140844Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2604.10678"},"observation_digest":"sha256:1687ca5d0eceb38ab26c7342248f14a9f24c1db6b68daaf8397c90a929db70bf","observation_id":"7c89bbcb-58b6-4cf8-9c5b-2dc6acfc981c","resolution":{"observed_at":"2026-05-11T10:51:03.817149Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":"2002.10619","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-06-30T16:44:56.408203Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":"e98a59b1-a0f5-4d83-afb4-c201efeddeb8","year":2002},"citing_paper":{"arxiv_id":"2606.30615","last_updated":"2026-06-29T17:49:33Z","snapshot_observed_at":"2026-07-07T00:04:25.385438Z","submitted_at":"2026-06-29T17:49:33Z","title":"Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage","version":1},"reference_index":154,"source":"arxiv_source","source_observed_at":"2026-06-30T04:41:41.370083Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2606.30615"},"observation_digest":"sha256:d00f2f71d3e4a6c79c22e02bb467f02ee05db50d017b2bb38aa69884bd13d6d8","observation_id":"62524930-1de6-4068-9b3f-9d28be7c6d02","resolution":{"observed_at":"2026-06-30T16:44:56.409494Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2002.10619/citation-record","integrity":"/paper/2002.10619/integrity","json":"/paper/2002.10619/citation-record.json","paper":"/paper/2002.10619"},"outbound":[],"paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2002.10619."}