{"as_of":"2026-08-08T15:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19a2ebf181190985ce7d5ea303e39345c85cf379e28cd2c09e9f2c5d6be86aac","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T23:36:53.818667Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2502.08829/citation-record","integrity":"/paper/2502.08829/integrity","json":"/paper/2502.08829/citation-record.json","paper":"/paper/2502.08829"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-07-06T08:41:25.156353Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-07T23:36:53.658114Z","title":"Federated learning with personalization layers","venue":null,"work_id":null,"year":1912},"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":1,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.658114Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:5608a71ea8cfb7025aa495fe059fe531fa515d3fc2c335379a274a541b894afa","observation_id":"774dfc50-83db-41c9-9316-f94edad832e5","resolution":{"observed_at":"2026-08-07T23:36:53.658114Z","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":"2026-08-07T23:36:54.225162Z","title":"Federated learning with hierarchical clustering of local updates to improve training on non-iid data","venue":null,"work_id":"ab962974-5b98-412e-944f-92cf668d5bfa","year":2020},"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":2,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.662725Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:4068cdf545b20441ec53efbae2dcbe5b8b820160c19b00f3612b00f161e56b53","observation_id":"9422b5e1-68be-46f0-90c1-3b557ed21384","resolution":{"observed_at":"2026-08-07T23:36:54.228441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T23:36:53.666170Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"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":3,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.666170Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:f39add69657394652d6ba2b175ab5443d7928ae14b3323e032985c2805f72115","observation_id":"759940c4-f894-4115-ad75-8b6156a169b9","resolution":{"observed_at":"2026-08-07T23:36:53.666170Z","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":"2026-08-07T23:36:53.670049Z","title":"Entropy-sgd: Biasing gradient descent into wide valleys","venue":null,"work_id":null,"year":2019},"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":4,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.670049Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:aaa9fca9df330d46af0344d072c887d9a1dcec2d4deda9fe995036ec69c2161e","observation_id":"aecae38f-fa0c-4aaa-b8d8-704ff0392d96","resolution":{"observed_at":"2026-08-07T23:36:53.670049Z","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":"2026-08-07T23:36:54.211055Z","title":"Shallowing deep networks: Layer-wise pruning based on feature representations","venue":null,"work_id":"5044b34c-240a-4434-89dc-aaf1bf528d62","year":2018},"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":5,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.673624Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:e996d4322407c427d783e10afe5dfd0a60efa298888563c49b0386e1667bc001","observation_id":"6856e49f-b71a-4331-a1d4-0848414ba72b","resolution":{"observed_at":"2026-08-07T23:36:54.214373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2205.14840","last_updated":"2023-02-04T19:07:35Z","snapshot_observed_at":"2026-08-03T18:30:42.899927Z","submitted_at":"2022-05-30T04:03:31Z","title":"Maximizing Global Model Appeal in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14840","snapshot_observed_at":"2026-08-07T23:36:53.677220Z","title":"To federate or not to federate: Incentivizing client participation in federated learning","venue":null,"work_id":null,"year":2022},"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":6,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.677220Z"},"links":{"cited_paper":"/paper/2205.14840","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:77b2775a7b39c936411e64a6d29776c14f19d8a825ae67777b97182538fa6cec","observation_id":"23c7a5ba-0a0a-443d-a9d6-097cc9bab451","resolution":{"observed_at":"2026-08-07T23:36:53.677220Z","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":"2026-08-07T23:36:53.680981Z","title":"Exploiting shared representations for personalized federated learning","venue":null,"work_id":null,"year":2021},"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":7,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.680981Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:1f59e0932928da85167ccb49394bca40096137a65c684d4417b141f9868db1af","observation_id":"fa278f13-5380-4e67-bdd5-b6c22681fe14","resolution":{"observed_at":"2026-08-07T23:36:53.680981Z","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":"2026-08-07T23:36:53.684247Z","title":"Federated learning for predicting clinical outcomes in patients with covid-19","venue":null,"work_id":null,"year":2021},"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":8,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.684247Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:a4e56c1a59b70eb04f8e7f8e9b99f7b80aaa6a34defb323e6bbbbb76001ae31a","observation_id":"7571b7f9-fc30-465e-8055-217357ddc67b","resolution":{"observed_at":"2026-08-07T23:36:53.684247Z","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":"2026-08-07T23:36:54.191793Z","title":"Statistical comparisons of classifiers over multiple data sets","venue":null,"work_id":"e4135c9a-4200-4f22-a6d0-c105373b1ff2","year":2006},"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":9,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.687526Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:eb389f0039b30b184c77db4639b5a0ff810d0cf502fd36f99ebb5063f0f96a66","observation_id":"07268093-f785-4ce2-a058-114c226a3c23","resolution":{"observed_at":"2026-08-07T23:36:54.195149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2102.07148","last_updated":"2022-10-11T11:54:45Z","snapshot_observed_at":"2026-07-06T10:41:11.114777Z","submitted_at":"2021-02-14T13:19:43Z","title":"A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.07148","snapshot_observed_at":"2026-08-07T23:36:53.691109Z","title":"Fedu: A unified framework for federated multi-task learning with laplacian regularization","venue":null,"work_id":null,"year":2021},"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":10,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.691109Z"},"links":{"cited_paper":"/paper/2102.07148","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:1b92fd0d8cabbc371950bdf29fc3b5ec0fa4051b9838255f0f67f2d229464ddc","observation_id":"c689d801-e7a4-4533-b415-869fa4519702","resolution":{"observed_at":"2026-08-07T23:36:53.691109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.13173","last_updated":"2021-07-28T05:30:17Z","snapshot_observed_at":"2026-07-06T11:33:12.096573Z","submitted_at":"2021-07-28T05:30:17Z","title":"New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning","version":1},"cited_work":{"arxiv_id":"2107.13173","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.13173","snapshot_observed_at":"2026-08-07T23:36:53.955247Z","title":"New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning","venue":"cs.LG","work_id":"c76b0b25-306f-4eba-ae2f-6149b09130c4","year":2021},"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":11,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.694626Z"},"links":{"cited_paper":"/paper/2107.13173","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:afa335780deb799bd91df112816851241c9b77d674cfa90c3867d788abe01891","observation_id":"8b189b70-910f-4d30-a0c0-1bc81ae0bea4","resolution":{"observed_at":"2026-08-07T23:36:53.958891Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.698268Z","title":"Learning to prune deep neural networks via layer-wise optimal brain surgeon","venue":null,"work_id":null,"year":2017},"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":12,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.698268Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:e0cbcb557e534784c83306f21fb07348fc43b6ee9ac692753dfa58547203c000","observation_id":"9b13d847-cc1f-4ba3-b57c-ce6f6e49b1bb","resolution":{"observed_at":"2026-08-07T23:36:53.698268Z","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":"2026-08-07T23:36:54.177263Z","title":"Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications","venue":null,"work_id":"35a6ee94-6ac9-4f6b-8a3c-2d3e60ae9f34","year":2019},"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":13,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.701450Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:586bc69ac8fea6383a13129be38c3c21b15696abbc5fbd24bc109d79c09fa682","observation_id":"e22fe6e6-356a-4f25-82a9-a75363d0a275","resolution":{"observed_at":"2026-08-07T23:36:54.180678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2108.12594","last_updated":"2021-08-28T07:51:47Z","snapshot_observed_at":"2026-08-04T08:00:33.475943Z","submitted_at":"2021-08-28T07:51:47Z","title":"Layer-wise Model Pruning based on Mutual Information","version":1},"cited_work":{"arxiv_id":"2108.12594","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.12594","snapshot_observed_at":"2026-08-07T23:36:53.940406Z","title":"Layer-wise Model Pruning based on Mutual Information","venue":"cs.CL","work_id":"7841f76e-4772-404c-b126-ca077b9cddea","year":2021},"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":14,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.704611Z"},"links":{"cited_paper":"/paper/2108.12594","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:9e787132b00950511cd04668c40f14dc7a38e117c468db25741a443f02ccd6d1","observation_id":"61532007-20dd-46f1-afe2-7c711b7906a0","resolution":{"observed_at":"2026-08-07T23:36:53.945588Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.708017Z","title":"Learning both weights and connections for efficient neural network","venue":null,"work_id":null,"year":2015},"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":15,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.708017Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:fa56e7c54ab3ba8b3278aae3e05dd2d7a06d7ae591503f0d518a6ef254e0edbd","observation_id":"e78d0565-e73a-49ee-85f6-276b0fa1ee69","resolution":{"observed_at":"2026-08-07T23:36:53.708017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.12949","last_updated":"2022-06-26T19:49:41Z","snapshot_observed_at":"2026-08-07T21:39:31.834964Z","submitted_at":"2022-06-26T19:49:41Z","title":"Cross-Silo Federated Learning: Challenges and Opportunities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.12949","snapshot_observed_at":"2026-08-07T23:36:53.711160Z","title":"Cross-silo federated learning: Challenges and opportunities","venue":null,"work_id":null,"year":2022},"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":16,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.711160Z"},"links":{"cited_paper":"/paper/2206.12949","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:da0d393f49f4a4118a3b8fa377e8ba02c27bc524803d4c9a5ce0a9614ee8217a","observation_id":"dac946a5-c324-467c-a972-1e4d6bec6dab","resolution":{"observed_at":"2026-08-07T23:36:53.711160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02178","last_updated":"2019-12-04T18:58:26Z","snapshot_observed_at":"2026-07-06T08:42:06.688732Z","submitted_at":"2019-12-04T18:58:26Z","title":"Fantastic Generalization Measures and Where to Find Them","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02178","snapshot_observed_at":"2026-08-07T23:36:53.714535Z","title":"Fantastic generalization measures and where to find them","venue":null,"work_id":null,"year":1912},"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":17,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.714535Z"},"links":{"cited_paper":"/paper/1912.02178","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:fcaed450e303a3076b32ae5179e9be0ddcf26a6ff0340a0a60a246be1b638833","observation_id":"a3778d96-aa0b-47b2-b6a2-3f7e76d3a5a5","resolution":{"observed_at":"2026-08-07T23:36:53.714535Z","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":"2026-08-07T23:36:53.717903Z","title":"Mimic-iii, a freely accessible critical care database","venue":null,"work_id":null,"year":2016},"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":18,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.717903Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:c7c0d48c2753617906de9701d820239fa8da76eb658a06ea229d4de24d3fa050","observation_id":"ab2cc2b1-9b80-4254-b5bd-b2076551d162","resolution":{"observed_at":"2026-08-07T23:36:53.717903Z","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":"2026-08-07T23:36:54.158368Z","title":"Advances and open problems in federated learning","venue":null,"work_id":"a2292526-164a-4a22-a6fd-b021d6185ed6","year":2021},"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":19,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.721059Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:8948a4f2e179d21fc5a2ead112efcfb0ac7b050c9003f7f5fe581ef09ad11a3c","observation_id":"532255cd-af7b-4dfd-9a4e-06021098813f","resolution":{"observed_at":"2026-08-07T23:36:54.161649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.724285Z","title":"Similarity of neural network representations revisited","venue":null,"work_id":null,"year":2019},"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":20,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.724285Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:7f872e069f37179804a5c0f4fbaf82948f0fc60fd64bfbf244ece90e858f6223","observation_id":"1db8ee68-96b0-41aa-808e-0c1ce61c1b1d","resolution":{"observed_at":"2026-08-07T23:36:53.724285Z","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":"2026-08-07T23:36:53.727646Z","title":"Optimal brain damage","venue":null,"work_id":null,"year":1989},"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":21,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.727646Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:9f9f1ee50f55b995b09db17240475f4e9895604595fcade25e977550e84a5796","observation_id":"3401cd63-3d9d-425b-86c0-1c6240c8fa09","resolution":{"observed_at":"2026-08-07T23:36:53.727646Z","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":"2026-08-07T23:36:54.139636Z","title":"Layer-wise adaptive model aggregation for scalable federated learning","venue":null,"work_id":"a48b5b3d-a17c-43a1-8638-ee9ead8b022c","year":2023},"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":22,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.730850Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:99fd260ceadae9feb4ec326577916a492a63cfedc940c228d670c49421efec1c","observation_id":"4cc4785e-56c5-43b0-a4cd-a6072385674a","resolution":{"observed_at":"2026-08-07T23:36:54.142887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:54.130651Z","title":"Federated learning on non-iid data silos: An experimental study","venue":null,"work_id":"68c4a52a-3d7f-4a9e-a57d-f4802b8084f9","year":2022},"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":23,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.733939Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:4bd55474372c525ccdde6fccb3deaf3e885d643eb639ecee51cb3e5f46a712ed","observation_id":"633220b3-569e-466d-905d-8378e3d8623c","resolution":{"observed_at":"2026-08-07T23:36:54.134033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.737243Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":null,"year":2020},"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":24,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.737243Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:60db89c382c2b28aded514e669be9fbd494858ad8ea3651092cb37482163e18d","observation_id":"66c1028d-7206-4a3e-8fa7-057299d84b31","resolution":{"observed_at":"2026-08-07T23:36:53.737243Z","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":"2026-08-07T23:36:54.116967Z","title":"Ditto: Fair and robust federated learning through personalization","venue":null,"work_id":"566107c1-7bf2-47a7-8842-3161f7db1d98","year":2021},"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":25,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.740388Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:4c2fda3a977fdf54804938eff6cb6dfcbd3237c72196b5bee5582e54893cc124","observation_id":"93a670fe-77a5-4e8d-be62-622943fd696b","resolution":{"observed_at":"2026-08-07T23:36:54.120226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1511.07543","last_updated":"2016-02-28T22:04:54Z","snapshot_observed_at":"2026-08-03T16:55:19.885866Z","submitted_at":"2015-11-24T02:31:46Z","title":"Convergent Learning: Do different neural networks learn the same representations?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07543","snapshot_observed_at":"2026-08-07T23:36:53.743544Z","title":"Convergent learning: Do different neural networks learn the same representations? arXiv preprint arXiv:1511.07543, 2015","venue":null,"work_id":null,"year":2015},"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":26,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.743544Z"},"links":{"cited_paper":"/paper/1511.07543","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:ba67589ba349c76c2c7f8172851d286fe5e56f2ea7a5a315a47697fa43c78f78","observation_id":"e3ba4703-c581-4a38-90f5-411891e04a61","resolution":{"observed_at":"2026-08-07T23:36:53.743544Z","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":"2026-08-07T23:36:53.746974Z","title":"Learning efficient convolutional networks through network slimming","venue":null,"work_id":null,"year":2017},"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":27,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.746974Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:5c874f5e6078736b56b7c3db5fb5800f5a554462cf46890ae1ed2bad7b450121","observation_id":"d2034eb1-feee-4019-92b5-915cc7cb3787","resolution":{"observed_at":"2026-08-07T23:36:53.746974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05270","last_updated":"2019-03-05T05:58:11Z","snapshot_observed_at":"2026-07-06T07:07:34.689643Z","submitted_at":"2018-10-11T22:15:28Z","title":"Rethinking the Value of Network Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05270","snapshot_observed_at":"2026-08-07T23:36:53.749872Z","title":"Rethinking the value of network pruning","venue":null,"work_id":null,"year":2018},"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":28,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.749872Z"},"links":{"cited_paper":"/paper/1810.05270","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:9860660a11dd4ad17bc1f09e50a37f6b8c9ab432b461abc4e4befcd41986bf13","observation_id":"56ff05b4-2e3a-483b-a663-5413d35fd6e3","resolution":{"observed_at":"2026-08-07T23:36:53.749872Z","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":"2026-08-07T23:36:54.103111Z","title":"Layer-wised model aggregation for personalized federated learning","venue":null,"work_id":"a3521c97-33d4-4ee8-b800-0237b1f4b004","year":2022},"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":29,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.753138Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:a6eff785b9e3cdee68be55ce977f1c9c020e43107bf9bb886d0a6d50269c6fa4","observation_id":"64235214-5a09-4437-b5be-3f290be1926d","resolution":{"observed_at":"2026-08-07T23:36:54.106342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:54.093932Z","title":"Understanding deep convolutional networks","venue":null,"work_id":"e1f024f9-6987-4a0a-86d9-001fa88c1833","year":2016},"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":30,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.755958Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:9b7c9c84751c46433d1813789f20b020ec9a63ed4195ec2a6924d0848395b133","observation_id":"2a561f5e-f379-4559-8704-bf522f5e4d7e","resolution":{"observed_at":"2026-08-07T23:36:54.097253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.762017Z","title":"Communication- efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"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":32,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.762017Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:534a91a5ac354ecfc46d98d2f6a58c67f75f5706771bd3651f139e6ae1950b24","observation_id":"8577d73e-001a-47fc-82dc-df594431de4c","resolution":{"observed_at":"2026-08-07T23:36:53.762017Z","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":"2026-08-07T23:36:53.765026Z","title":"Importance estimation for neural network pruning","venue":null,"work_id":null,"year":2019},"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":33,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.765026Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:bbd903fe959c7357a1b640246f6b02d47544a1cd443bbfb2d5a80fc945994376","observation_id":"c97bfc0e-f96f-43f5-b676-ad9fac40796b","resolution":{"observed_at":"2026-08-07T23:36:53.765026Z","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":"2026-08-07T23:36:54.074340Z","title":"Insights on representational similarity in neural networks with canonical correlation","venue":null,"work_id":"e9e1fbf2-b2e5-4273-9e63-3958d3c47c15","year":2018},"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":34,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.767969Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:1c73dee99b85a666caa287cf75a4b13cc4d862cb0d80b3284e88dea1d8381fe2","observation_id":"9d18b2c4-c709-47b2-b686-ce481f016c7b","resolution":{"observed_at":"2026-08-07T23:36:54.077726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2106.06042","last_updated":"2022-03-16T03:50:49Z","snapshot_observed_at":"2026-07-06T11:18:09.557612Z","submitted_at":"2021-06-04T04:34:26Z","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06042","snapshot_observed_at":"2026-08-07T23:36:53.770914Z","title":"Fedbabu: Towards enhanced representation for federated image classification","venue":null,"work_id":null,"year":2021},"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":35,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.770914Z"},"links":{"cited_paper":"/paper/2106.06042","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:7c705ef2e024cd275d92881262b085cbb4c2117ad3c1d9a171fcad9ed02f77fd","observation_id":"87056410-1459-45be-92f3-393993b7c96b","resolution":{"observed_at":"2026-08-07T23:36:53.770914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.05874","last_updated":"2021-05-14T00:54:23Z","snapshot_observed_at":"2026-08-05T01:58:15.930347Z","submitted_at":"2021-05-12T18:00:20Z","title":"The Federated Tumor Segmentation (FeTS) Challenge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.05874","snapshot_observed_at":"2026-08-07T23:36:53.773969Z","title":"The federated tumor segmentation (fets) challenge","venue":null,"work_id":null,"year":2021},"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":36,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.773969Z"},"links":{"cited_paper":"/paper/2105.05874","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:a4eea73be88dab034f19c087612b2d334d1bc3024bcdc97cc2456c28dee944cc","observation_id":"5cd1fb33-4c7b-4f05-a5e1-618227e13a2f","resolution":{"observed_at":"2026-08-07T23:36:53.773969Z","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":"2026-08-07T23:36:54.065365Z","title":"Federated learning enables big data for rare cancer boundary detection","venue":null,"work_id":"260f1279-d5a0-482d-b702-6c4372f3052f","year":2022},"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":37,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.777341Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:38940adcf1b799c332230de63bf7ad501698f7fc9eb7b8ab3717dbf88ee37a4f","observation_id":"e3aa18f1-b982-453e-98b3-faa2cc71f7ae","resolution":{"observed_at":"2026-08-07T23:36:54.068726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:54.056421Z","title":"Pruning algorithms-a survey","venue":null,"work_id":"9d2cf10e-234c-448d-9211-fdd23dff5a81","year":1993},"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":38,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.780199Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:c6c3403f82bc5c36048f9a9e828d6799b582d2a2082cb9da464548753f5390d1","observation_id":"a4064a12-b823-44b4-92ad-17344ef9ad8a","resolution":{"observed_at":"2026-08-07T23:36:54.059494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1312.6034","last_updated":"2014-04-19T11:54:52Z","snapshot_observed_at":"2026-07-06T03:31:30.452356Z","submitted_at":"2013-12-20T16:45:54Z","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6034","snapshot_observed_at":"2026-08-07T23:36:53.783238Z","title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","venue":null,"work_id":null,"year":2013},"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":39,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.783238Z"},"links":{"cited_paper":"/paper/1312.6034","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:62e98c0257cd4673e7bda4bf04e7f3393395f4c43f5ade45f14040e2719d3405","observation_id":"06f94a2f-31b3-41db-a568-5f19cf5f959e","resolution":{"observed_at":"2026-08-07T23:36:53.783238Z","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":"2026-08-07T23:36:53.786504Z","title":"Federated multi-task learning","venue":null,"work_id":null,"year":2017},"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":40,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.786504Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:6c226156cad5125a43100206c4db578c7920595fe02f42756f8425a766439df2","observation_id":"8556f1d0-6010-4087-aa9e-400a6cf14eb5","resolution":{"observed_at":"2026-08-07T23:36:53.786504Z","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":"2026-08-07T23:36:54.042267Z","title":"Personalized federated learning with moreau envelopes","venue":null,"work_id":"a46345a3-8f85-4c3d-854c-847eafd89790","year":2020},"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":41,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.789399Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:3613f4796ce721246e5b6ad67fb15d29831e374225cf99d2b5e94fc84cefc0f2","observation_id":"93446250-4b13-4cf4-986d-8f7e11af0a1e","resolution":{"observed_at":"2026-08-07T23:36:54.045578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2210.04620","last_updated":"2023-05-05T08:48:12Z","snapshot_observed_at":"2026-07-06T14:03:01.528958Z","submitted_at":"2022-10-10T12:17:30Z","title":"FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.04620","snapshot_observed_at":"2026-08-07T23:36:53.792366Z","title":"Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings","venue":null,"work_id":null,"year":null},"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":42,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.792366Z"},"links":{"cited_paper":"/paper/2210.04620","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:30102775df4d61b2e86680766401e3e8de3a838f4dc54243dd857d17168a17b0","observation_id":"1b6b57a1-75f9-4f02-9e9f-155e03d82c78","resolution":{"observed_at":"2026-08-07T23:36:53.792366Z","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":"2026-08-07T23:36:54.032875Z","title":"Towards personalized federated learning via heterogeneous model reassembly","venue":null,"work_id":"6228cb29-037d-4b51-96aa-097f4416dbc4","year":2024},"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":43,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.795965Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:cdde98ee666d0b0f87d7991074d18e9001bc4dfe53a5327b951c144abf06bbe8","observation_id":"f4480df2-3305-4b32-9b4b-f27cc7b7f820","resolution":{"observed_at":"2026-08-07T23:36:54.036463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:54.023720Z","title":"Towards understanding learning representations: To what extent do different neural networks learn the same representation","venue":null,"work_id":"aa2463a8-6e56-415c-8217-6dc3cbb90cb9","year":2018},"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":44,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.799204Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:521d342fdb2195e052aeeb489a27b8fa7d17fb631db982d04e3cef5bfe0460e4","observation_id":"b92c4762-b798-453a-af66-cc0263b2b2e9","resolution":{"observed_at":"2026-08-07T23:36:54.027012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:54.014481Z","title":"Generalized shape metrics on neural representations","venue":null,"work_id":"1cb6bc63-d42d-4c3d-851d-5847627b238f","year":2021},"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":45,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.802480Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:00c2d584a976b9e72a8a5034e1fed176ae192f21cebf186aac5f61de473f50f2","observation_id":"30df154d-d7c2-4a0a-a43d-01272eb75b13","resolution":{"observed_at":"2026-08-07T23:36:54.017772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:36:53.805594Z","title":"How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014","venue":null,"work_id":null,"year":2014},"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":46,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.805594Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:5d58a404ce8eb5fcbffd371541febfbc50baf52c9314038ed90674c8081e5a1a","observation_id":"334dfa1d-7ca0-4cf3-ac50-d65d38838fdf","resolution":{"observed_at":"2026-08-07T23:36:53.805594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.04758","last_updated":"2022-03-03T23:28:58Z","snapshot_observed_at":"2026-07-06T08:56:39.004549Z","submitted_at":"2020-02-12T01:56:16Z","title":"Salvaging Federated Learning by Local Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.04758","snapshot_observed_at":"2026-08-07T23:36:53.808870Z","title":"Salvaging federated learning by local adaptation","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":47,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.808870Z"},"links":{"cited_paper":"/paper/2002.04758","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:bff02a7ad37a8986d1b9f91193100b02e8e3a5850fcbb6f37acf1ddfd64bab59","observation_id":"a73aeed1-4d31-4641-a0b6-f139ee9d74ee","resolution":{"observed_at":"2026-08-07T23:36:53.808870Z","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":"2026-08-07T23:36:53.812182Z","title":"Visualizing and understanding convolutional networks","venue":null,"work_id":null,"year":2014},"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":48,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.812182Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:0c73ba3bcc51698d635357288b180ba3e92582b5cc8ee2f88cace756894c0fa7","observation_id":"f4616968-d617-4f3a-86ef-8b28ad94b200","resolution":{"observed_at":"2026-08-07T23:36:53.812182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-07-06T06:42:35.645776Z","submitted_at":"2018-06-02T04:45:58Z","title":"Federated Learning with Non-IID Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00582","snapshot_observed_at":"2026-08-07T23:36:53.815320Z","title":"Federated learning with non-iid data","venue":null,"work_id":null,"year":2018},"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":49,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.815320Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:e023b5eb1918879e3751754ca857b194643579c5c511842e9f2eae4ce860f6bf","observation_id":"3bf3ac74-f275-45e8-8bba-f14c44216279","resolution":{"observed_at":"2026-08-07T23:36:53.815320Z","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":"2026-08-07T23:36:53.995379Z","title":"Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning","venue":null,"work_id":"6d378e79-dead-470b-b11d-eabb1648b14f","year":2023},"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":50,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.818667Z"},"links":{"citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:e521f7f5b61c24d75a69f767b83a48b367c2452427fb4a0b9841865a436cbc95","observation_id":"732daae1-544d-41c4-9102-d71e18b0563b","resolution":{"observed_at":"2026-08-07T23:36:53.998737Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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"}}],"paper":{"arxiv_id":"2502.08829","last_updated":"2026-07-19T01:09:13Z","latest_version":2,"primary_category":"cs.LG","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"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":2,"verified_fuzzy":17},"total_outbound_references":50},"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 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2502.08829."}