{"as_of":"2026-08-16T02:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b22b27bc76dc9b76259207be9c5e1d5a2de6a2dded2655737a11ec248e3d0511","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:30:16.377276Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.00452/citation-record","integrity":"/paper/2412.00452/integrity","json":"/paper/2412.00452/citation-record.json","paper":"/paper/2412.00452"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:30:16.178583Z","title":"O’Connor, and Kevin McGuinness","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.178583Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:6382ae873cb0d3dbe3c4c985c6b3769488bc8043ed6c2e8f1d78cb7150df62a2","observation_id":"0a1775a6-ec76-4e24-9df1-22c53e140cf2","resolution":{"observed_at":"2026-08-12T05:30:16.178583Z","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":"2026-08-12T05:30:16.184404Z","title":"Kanwal, Tegan Maharaj, Asja Fischer, Aaron C","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.184404Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:5ae7a71f65376cbdb76f65f01cc77a5fbb38695e7f508bb2295f583bc8c20669","observation_id":"f43ec152-5321-4c9f-9dc3-8c9e272fe8ce","resolution":{"observed_at":"2026-08-12T05:30:16.184404Z","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":"2026-08-12T05:30:16.189898Z","title":"Goodfellow, Nico- las Papernot, Avital Oliver, and Colin Raffel","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.189898Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:f18549743dd4ae11eafde87c78569a01e0c57d095631859a8984d7feb63a11a6","observation_id":"5c3713e3-5470-45de-8d13-5e05cb9f96a1","resolution":{"observed_at":"2026-08-12T05:30:16.189898Z","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":"2026-08-12T05:30:16.194935Z","title":"Exploring Simple Siamese Representation Learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.194935Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:6e392653fafb01fc5edfa3d06dae4fbcc5e1017deee5bb0501801b7f096e18a1","observation_id":"a19f4247-49f9-4678-9e6f-76082e06b418","resolution":{"observed_at":"2026-08-12T05:30:16.194935Z","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":"2026-08-12T05:30:16.199822Z","title":"Learning with Instance-Dependent La- bel Noise: A Sample Sieve Approach","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.199822Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:1f6da20a9fbd326d56dae3053ca248d09a661ac1b85325736a83ad321ad51f86","observation_id":"a2df9f26-6d91-47c7-b1ae-84b9603c89fb","resolution":{"observed_at":"2026-08-12T05:30:16.199822Z","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":"2026-08-12T05:30:16.204562Z","title":"RandAugment: Practical Automated Data Augmenta- tion with a Reduced Search Space","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.204562Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:9a050db9f05a662cb6177a1933977c1fd9457e6bf730b3f2085c5eef663a5f40","observation_id":"54ffc3b9-e9a7-46f7-86e9-c8af8d7a29ce","resolution":{"observed_at":"2026-08-12T05:30:16.204562Z","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":"2026-08-12T05:30:16.209917Z","title":"Tsang, and Masashi Sugiyama","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.209917Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:232044c4ad63b6bf934552a8f6d7b5adb4b82ec1ca9468f34251975ddacc51b1","observation_id":"17d3d769-2a10-422a-abe5-371b1c7bc471","resolution":{"observed_at":"2026-08-12T05:30:16.209917Z","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":"2026-08-12T05:30:16.214556Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.214556Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:72a3539374a0ebc7eb1bcb0b48d89fcd908bb95e2a6f35710b00c34c42c3c16f","observation_id":"bf4ee217-e3a5-489b-aaa0-0f0649b9ae85","resolution":{"observed_at":"2026-08-12T05:30:16.214556Z","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":"2026-08-12T05:30:16.219471Z","title":"Ball, Katie S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.219471Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:75f4ce318593d19ecf7d2fa508700a619b2ca0a2337886fce2743c101ad2a281","observation_id":"bc95955c-e763-4cc2-97e7-8f295bbe682d","resolution":{"observed_at":"2026-08-12T05:30:16.219471Z","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":"2026-08-12T05:30:16.224343Z","title":"FedFixer: Mitigating Hetero- geneous Label Noise in Federated Learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.224343Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:99e0b6b4c9aa86016ecf96aae77bf3c3ed39d53f503d604193503ba18b6502e4","observation_id":"a32833b8-5f90-4efb-ae18-64dcd7f326e2","resolution":{"observed_at":"2026-08-12T05:30:16.224343Z","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":"2026-08-12T05:30:16.230056Z","title":"To- wards Federated Learning against Noisy Labels via Local Self-Regularization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.230056Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:2f06ac5a81ce345f633a7cffe06c608c879ab46a62266c9a665a475f504d9113","observation_id":"7c6e24f8-0b4c-4557-a961-134b9c763f98","resolution":{"observed_at":"2026-08-12T05:30:16.230056Z","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":"2026-08-12T05:30:16.236339Z","title":"Tack- ling Noisy Clients in Federated Learning with End-to-end Label Correction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.236339Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:60ad6afb99ffa047d91d7628492fa400f86544d47243e310cb085fca003e614a","observation_id":"18e52b66-8e6c-45b4-bda6-3e8e0de9ab0f","resolution":{"observed_at":"2026-08-12T05:30:16.236339Z","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":"2026-08-12T05:30:16.241480Z","title":"Makowski, Daniel Rueckert, and Rickmer Braren","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.241480Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:c0f8529f993b33a07ae5fe6ed32dd503be74c248e2a0d0b39d3e7a2eca818193","observation_id":"5aebaf84-bf2f-46fc-a915-6c466b6e91d1","resolution":{"observed_at":"2026-08-12T05:30:16.241480Z","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":"2026-08-12T05:30:16.246327Z","title":"FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.246327Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:2289ccc0af47972e43201fac9f55825c69ac9bc3ad40fff1162b31f62eadcc93","observation_id":"ee8dbc09-6fa0-47f7-b835-521392ac7d10","resolution":{"observed_at":"2026-08-12T05:30:16.246327Z","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":"2026-08-12T05:30:16.251712Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.251712Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:eb085511cd27ddd9ad82619cd412975c36d868870148381ed54622e619d1c586","observation_id":"13222e19-6c30-46b7-aa47-9e281078329f","resolution":{"observed_at":"2026-08-12T05:30:16.251712Z","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":"2026-08-12T05:30:16.256417Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.256417Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:9b5ff63219964e957e58991e1b434544af2ac081516f6ffd71279750eaa99e71","observation_id":"2a7e8561-a040-4cf3-a432-26acccfeb3c9","resolution":{"observed_at":"2026-08-12T05:30:16.256417Z","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":"2026-08-12T05:30:16.262743Z","title":"FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.262743Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:99ce0f12a5dcb0ba84f8194d15cef33c84fca998a20ee49316d95bc2f5e0ff8a","observation_id":"f250919f-4ea8-47f9-bd43-7529d559f838","resolution":{"observed_at":"2026-08-12T05:30:16.262743Z","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":"2026-08-12T05:30:16.267897Z","title":"Fed- erated learning on non-iid data silos: An experimental study","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.267897Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:852be6f6e375cb8e6483edf962d4e07d8ab3733f338a1a09bd1f8e529490ea70","observation_id":"d9f358d0-76d8-410d-8089-644cddcf945e","resolution":{"observed_at":"2026-08-12T05:30:16.267897Z","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":"2026-08-12T05:30:16.273384Z","title":"Federated Optimization in Heterogeneous Networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.273384Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:093b69f77a48d40ceb3a9098dfc8c3ab97c6aedcbd76174b3aacd2c0c2675e53","observation_id":"dec900cb-ba5f-42fa-8f34-d8efe50d0e59","resolution":{"observed_at":"2026-08-12T05:30:16.273384Z","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":"2026-08-12T05:30:16.279073Z","title":"Provably End-to-end Label-noise Learning with- out Anchor Points","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.279073Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:91f7ceac8dc5c543b58f53974ec7a79ffa091bdf521299aaeee44939d40674f5","observation_id":"d50165d8-7cd1-4770-94b5-de2d950a1c13","resolution":{"observed_at":"2026-08-12T05:30:16.279073Z","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":"2026-08-12T05:30:16.284096Z","title":"Federated Learn- ing with Extremely Noisy Clients via Negative Distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.284096Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:ecab1c0f87daa9728b4ba3e359bad88f934ee7025c8899ecbdb98bae6db4aa1b","observation_id":"635b90da-b089-45d8-ae61-fb2488480c12","resolution":{"observed_at":"2026-08-12T05:30:16.284096Z","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":"2026-08-12T05:30:16.289815Z","title":"Dick, and Akhil Mathur","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.289815Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:21e040ae678c2b887ae29ab58134b5d50559a31d676bf90535baf66b99026c66","observation_id":"8c4e43de-54d8-42a8-ba43-f4ecb8f3bfe1","resolution":{"observed_at":"2026-08-12T05:30:16.289815Z","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":"2026-08-12T05:30:16.294887Z","title":"Communication- Efficient Learning of Deep Networks from Decentralized Data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.294887Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:6a88d1aac7391f93a6107b3f12cc1c06ade24b987c45c9d8f9ad15d2e3b390e9","observation_id":"8fdb3bd6-bc20-49ab-9835-14b117114c36","resolution":{"observed_at":"2026-08-12T05:30:16.294887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.10694","last_updated":"2018-02-26T16:51:57Z","snapshot_observed_at":"2026-08-14T20:57:15.629420Z","submitted_at":"2017-05-30T15:10:51Z","title":"Deep Learning is Robust to Massive Label Noise","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.10694","snapshot_observed_at":"2026-08-12T05:30:16.299688Z","title":"Belongie, and Nir Shavit","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.299688Z"},"links":{"cited_paper":"/paper/1705.10694","citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:60fa1657ff179d57652814609b45d68889cd17c83784666aa8b67ddff8faa190","observation_id":"da1c2c92-392f-42ed-9f70-ee743475b540","resolution":{"observed_at":"2026-08-12T05:30:16.299688Z","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":"2026-08-12T05:30:16.305731Z","title":"FixMatch: Simplifying Semi- Supervised Learning with Consistency and Confidence","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.305731Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:be97f2af6795f77a3969ae8c99dee36f40d0421f6a8383cebcfdc03b9ec9f394","observation_id":"43db64e0-24fe-43bf-95d6-6cf1aa4fc9b7","resolution":{"observed_at":"2026-08-12T05:30:16.305731Z","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":"2026-08-12T05:30:16.310764Z","title":"Learning From Noisy Labels With Deep Neural Networks: A Survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.310764Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:ae15fbe00b3e97964341407ca13da516393d9685d56efb060fde20b50b6b0eae","observation_id":"20d514ed-86d5-47f4-adb1-56fe22b5da37","resolution":{"observed_at":"2026-08-12T05:30:16.310764Z","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":"2026-08-12T05:30:16.316175Z","title":"A Survey on Federated Recommendation Systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.316175Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:ae77d37ff3710472a745f5f621802f70f0a4c0e7a990323ac50ce08be38357f2","observation_id":"6fe61fb0-c92b-4d35-9a10-1d07dcabca5c","resolution":{"observed_at":"2026-08-12T05:30:16.316175Z","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":"2026-08-12T05:30:16.321892Z","title":"FedCoop: Cooperative Federated Learning for Noisy Labels","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.321892Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:60508a700efa0f32b2aac35a2c46f81dcd5eebea9cf9a64d74a568ec2f789e5b","observation_id":"ccec7c2f-3dcc-4789-b949-b92838df639f","resolution":{"observed_at":"2026-08-12T05:30:16.321892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10110","last_updated":"2022-05-20T12:06:39Z","snapshot_observed_at":"2026-08-13T15:40:07.519654Z","submitted_at":"2022-05-20T12:06:39Z","title":"FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10110","snapshot_observed_at":"2026-08-12T05:30:16.327183Z","title":"FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.327183Z"},"links":{"cited_paper":"/paper/2205.10110","citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:2eea8259e540ae589c72f237416074655efbdc632976f10754f47a94d6f9b1f5","observation_id":"1ee43c68-fdc0-445a-97b4-f653c0080043","resolution":{"observed_at":"2026-08-12T05:30:16.327183Z","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":"2026-08-12T05:30:16.331949Z","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.331949Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:8948fb2383f57f837a42b4af9c95e47d2e929d8d7c115efa4c7e930dbcbfac2a","observation_id":"f8e7d467-f434-4bec-842a-476119072d30","resolution":{"observed_at":"2026-08-12T05:30:16.331949Z","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":"2026-08-12T05:30:16.336573Z","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.336573Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:c2acaa15f1d3f3a3d7b7a5a35b6dea24fa0e903d98872cda992b6ce81a207fe0","observation_id":"4af758b6-4359-43d8-abfb-31b967440045","resolution":{"observed_at":"2026-08-12T05:30:16.336573Z","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":"2026-08-12T05:30:16.341682Z","title":"Learning from massive noisy labeled data for image classification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.341682Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:eff4db1c49c0cbed27fc34ae8a348d648c444931abf495edd2451c92a4f746a9","observation_id":"1e964aab-bd6a-4885-9f76-c7f39e908506","resolution":{"observed_at":"2026-08-12T05:30:16.341682Z","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":"2026-08-12T05:30:16.346818Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.346818Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:328099f87578c97c64480b8b2fed7f3acfa0d93df8bbec3b128e25e1abc07e23","observation_id":"416f3f05-a9c1-491b-bf87-4c302cdc98de","resolution":{"observed_at":"2026-08-12T05:30:16.346818Z","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":"2026-08-12T05:30:16.351680Z","title":"Robust Federated Learning With Noisy La- bels","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.351680Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:d90e9f4d3c8b3bba001b1188e7b3f24bd6d6da7e6ab40de4b2d08b4a14efa6e3","observation_id":"4a9d925f-f8de-4cbe-868e-65cb55be2538","resolution":{"observed_at":"2026-08-12T05:30:16.351680Z","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":"2026-08-12T05:30:16.356150Z","title":"Tsang, and Masashi Sugiyama","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.356150Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:462c91ff76648b1d691bea4e815e365acf951b89ea2dd1a58ac9beb8996b8170","observation_id":"ea48567c-dd6b-4c57-954a-e50224469cf2","resolution":{"observed_at":"2026-08-12T05:30:16.356150Z","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":"2026-08-12T05:30:16.361433Z","title":"Understanding deep learning requires rethinking generalization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.361433Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:df9cf5c88cda3a08efb0d71687b48b2525abc55864c6b8145bde2d1e36e4f812","observation_id":"fba48851-e044-400b-bb21-94c1ded026bd","resolution":{"observed_at":"2026-08-12T05:30:16.361433Z","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":"2026-08-12T05:30:16.366203Z","title":"BadLabel: A Robust Per- spective on Evaluating and Enhancing Label-Noise Learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.366203Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:ed12dda83f72ec5c5a40ba51e6a85c8cd19aa3c5eeea9eba949259dd885aff32","observation_id":"7716c118-4c0b-461b-a1b4-5e0bde4f9a6f","resolution":{"observed_at":"2026-08-12T05:30:16.366203Z","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":"2026-08-12T05:30:16.472732Z","title":"Federated Label-Noise Learning with Local Diversity Product Regularization","venue":null,"work_id":"9e68a3ea-e397-4e97-8236-70f7dd569ce4","year":2024},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.371246Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:41814f5fe01fc71e732165d4003223518138628156f90ea7ee9effe6c13d2266","observation_id":"ee83a905-eadd-4a8b-9c6f-0db37c6578e9","resolution":{"observed_at":"2026-08-12T05:30:16.479134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:30:16.453430Z","title":"server → client","venue":null,"work_id":"e93ab9be-1841-4b22-b17c-9f4275f22815","year":null},"citing_paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T05:30:16.377276Z"},"links":{"citing_paper":"/paper/2412.00452"},"observation_digest":"sha256:c18a7f0eff26233c2f0e59fa25c0e9b26b853f94bd1a78e713fcf958f8dc8bca","observation_id":"d28964b1-2d9e-474e-896f-ba7572a67678","resolution":{"observed_at":"2026-08-12T05:30:16.460160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.00452","last_updated":"2026-05-28T16:18:55Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T05:20:29.016126Z","submitted_at":"2024-11-30T11:57:26Z","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2412.00452."}