{"as_of":"2026-08-07T19:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed544abf93c2c12479e92494ccc9fc06dd44aa03a3bed014b2c5ef1cdb42bd79","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:58:22.560816Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.10611/citation-record","integrity":"/paper/2507.10611/integrity","json":"/paper/2507.10611/citation-record.json","paper":"/paper/2507.10611"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:58:32.514771Z","title":"Vucinich and Q","venue":null,"work_id":"d9ab7b89-b761-4ccf-9c79-3ec731551428","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.556073Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:fbda7a95323593cf7e640678e7f1faba970b406aab182c31694b21a050963869","observation_id":"cabec715-ea3d-46c8-bd9f-caaf1f8ff037","resolution":{"observed_at":"2026-08-06T17:58:32.645653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06197","last_updated":"2024-08-12T14:48:25Z","snapshot_observed_at":"2026-07-06T18:59:38.952273Z","submitted_at":"2024-08-12T14:48:25Z","title":"Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption","version":1},"cited_work":{"arxiv_id":"2408.06197","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.06197","snapshot_observed_at":"2026-08-06T17:58:24.316590Z","title":"Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption","venue":"cs.CR","work_id":"c0f50cd0-3688-497f-9008-0f357e319001","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.616164Z"},"links":{"cited_paper":"/paper/2408.06197","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:52a9b06c76a0ffb32201b31f8bda72a762055156ecf22794eb0c23217d8e848f","observation_id":"0c45c774-2a1d-4a30-bc3e-ecb7af01fe30","resolution":{"observed_at":"2026-08-06T17:58:24.415883Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.07963","last_updated":"2019-12-02T19:00:11Z","snapshot_observed_at":"2026-08-06T20:09:23.048652Z","submitted_at":"2019-11-18T21:25:03Z","title":"Can You Really Backdoor Federated Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.07963","snapshot_observed_at":"2026-08-06T17:58:17.686012Z","title":null,"venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.686012Z"},"links":{"cited_paper":"/paper/1911.07963","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:51906324978bda9bffdb2e345a7262811e98f6fe96c365904e8300f63c25f5f3","observation_id":"2d0bbb71-264a-4fce-b01f-c039e807c87a","resolution":{"observed_at":"2026-08-06T17:58:17.686012Z","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":"2026-08-06T17:58:32.280718Z","title":"Hard sample a ware noise robust learning for histopathology image classificat ion,","venue":null,"work_id":"575dafae-7877-4125-bc89-5da370121eb8","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.788942Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:738a79a154a64b36f2d88c8dd7d12f30c88d38b5e7a4aaeaea4f0d8a14ad0571","observation_id":"fcd560bf-340c-4da5-9592-8ef77747d2b7","resolution":{"observed_at":"2026-08-06T17:58:32.395468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:32.014996Z","title":"Mendieta, T","venue":null,"work_id":"468a8007-037f-4b83-89ee-3987d4c2368f","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.801743Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:3b96a5aa9004865fba879fba080b7c34da3317bfe38e1e07a6ff5a8cadf07720","observation_id":"f0d4fc64-ecf1-409d-86cd-a554410452ba","resolution":{"observed_at":"2026-08-06T17:58:32.130609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:31.759266Z","title":"Communication-efficient learning of deep networks from de central- ized data,","venue":null,"work_id":"2060cf90-05ff-420b-a5f1-f06a1a1a5650","year":2017},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.831906Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:8361b4455ddc162d6b4bce4cdda6087b3175e9b0bfa9ffa34c429458688067a4","observation_id":"ed5160dc-7f35-4aaf-b525-7b8d7235ae5c","resolution":{"observed_at":"2026-08-06T17:58:31.884517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:31.487042Z","title":"Federated learning with extremely noisy clients via negative distillation,","venue":null,"work_id":"76d05314-cae8-42f6-90c2-28521e6115e6","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:17.966404Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:0cf17e1ad7bcd83df7b0b3d5ba173402b7c2a4ff3b0a3f3c2f02dd7a19205779","observation_id":"0eb7b404-0a73-4f8c-80cb-3657638d98fd","resolution":{"observed_at":"2026-08-06T17:58:31.607149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04301","last_updated":"2024-08-08T08:35:32Z","snapshot_observed_at":"2026-08-04T18:14:27.480113Z","submitted_at":"2024-08-08T08:35:32Z","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04301","snapshot_observed_at":"2026-08-06T17:58:18.065197Z","title":"Tackling noisy clients in federated learning with end- to-end label correction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.065197Z"},"links":{"cited_paper":"/paper/2408.04301","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:2a2cd90c7cb68283d1fb53759e25aeb78b04d285e7b3f1f1be53236597c9f714","observation_id":"bd58f5c1-3156-4a56-b6ff-589aa01630ec","resolution":{"observed_at":"2026-08-06T17:58:18.065197Z","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":"2026-08-06T17:58:31.191472Z","title":"FedDiv: Collabo rative noise filtering for federated learning with noisy labels,","venue":null,"work_id":"3cb7d103-dfee-44ee-8f62-dce71c2129ca","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.153011Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:9e706f49bdfc1265934e78b29cfebbdcc39f837f0e4745e386bac03c0aec0693","observation_id":"f5d4edfc-3f87-419a-b954-c0c376ebc98a","resolution":{"observed_at":"2026-08-06T17:58:31.336709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:30.872215Z","title":"A systematic stu dy of the class imbalance problem in convolutional neural networ ks,","venue":null,"work_id":"73b2838c-952f-46f0-bbd5-25d92883e9b8","year":2018},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.268570Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:1515a03264b30fb8e91970035946220e407a7d59a8f78fd67364f58615b0deb6","observation_id":"bedf6091-942f-47f9-acee-80e2f78ec670","resolution":{"observed_at":"2026-08-06T17:58:31.034748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:30.551381Z","title":null,"venue":null,"work_id":"c34bee82-a028-4c69-b503-19bb523e3d8d","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.381490Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:02de430bb9b4434dc3583a3a2940e05a9fdb2000524c9d125edf53fdcea109bc","observation_id":"9d9aa38a-f5d2-43c3-96c5-583665a37146","resolution":{"observed_at":"2026-08-06T17:58:30.721446Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:30.348497Z","title":"Understand- ing deep learning (still) requires rethinking generalizat ion,","venue":null,"work_id":"f557e803-47c2-4f86-a444-771c0e1c6395","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.459009Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:756f0f43dc3e1ad38f63a3e36d3312e742a3c334af7c53ab18d1219277304eb4","observation_id":"9d86bac1-eb49-4d7d-9ff9-6c4f1d7eb32c","resolution":{"observed_at":"2026-08-06T17:58:30.413934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:30.235571Z","title":"On the robustnes s of decision tree learning under label noise,","venue":null,"work_id":"688f70c7-4450-4b81-9baa-c0d16df31c7f","year":2017},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.575381Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:d7c473a1a4ce7c95017c250f48ef69f2b266baa9f0901edea726d842efa36c20","observation_id":"2c680b85-0a39-4984-a7c3-708f6c8f73b7","resolution":{"observed_at":"2026-08-06T17:58:30.288215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:30.002571Z","title":"Loss fa ctorization, weakly supervised learning and label noise robustness,","venue":null,"work_id":"03206a64-48cd-4623-ae39-04f5223a384a","year":2016},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.677219Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:e75baf5ade9a99d24c5b7b37a1d1ffa197e331091284381635746304fa4b4c99","observation_id":"413f86da-f8fa-4257-9d99-79f8d623dac0","resolution":{"observed_at":"2026-08-06T17:58:30.118621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.04406","last_updated":"2021-02-20T07:28:12Z","snapshot_observed_at":"2026-07-06T10:12:54.188337Z","submitted_at":"2020-11-09T13:16:02Z","title":"A Survey of Label-noise Representation Learning: Past, Present and Future","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.04406","snapshot_observed_at":"2026-08-06T17:58:18.808441Z","title":"A survey of label-noise representation learning: Past, pres ent and future,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.808441Z"},"links":{"cited_paper":"/paper/2011.04406","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:2ad938a48974243e90fced9d9c602acd2cffaa8a23923c7a5dcda701f89b8d39","observation_id":"00d3009a-36d2-4c0c-a289-8081274bf6af","resolution":{"observed_at":"2026-08-06T17:58:18.808441Z","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":"2026-08-06T17:58:29.862761Z","title":"Learnin g from noisy labels with deep neural networks: A survey,","venue":null,"work_id":"1f780496-f856-4ab1-9369-3fff40822840","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:18.919462Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:166e38c84409d4f775d6103e14de9767af80b37581b2edb4ba43d1173eec1464","observation_id":"0eeb85c9-c305-4cd8-8220-837c5db965ac","resolution":{"observed_at":"2026-08-06T17:58:29.929625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:29.675906Z","title":"Weakly supervised learning with side information for noisy labeled images,","venue":null,"work_id":"efe4ce5a-334b-426c-8988-06734bab945f","year":2020},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.066988Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:93c495222c41f7eef2721a19f236a1c27a6f831a0e777dd2c79e7da4cb0cb22f","observation_id":"33d1b661-f4e2-4d2a-8506-f7b626e5b6d7","resolution":{"observed_at":"2026-08-06T17:58:29.782895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:29.530307Z","title":"Lea rning with noisy labels via sparse regularization,","venue":null,"work_id":"331d1318-75b4-4e09-acf3-29cec5fd3e2a","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.150369Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:77fa2b3a6e8a6ff0341ddd447860c3cdce1fe9a20e77c3cd62347212a9ddf45e","observation_id":"ec0f480a-1cc2-4f96-8fb7-bccac1ef03ec","resolution":{"observed_at":"2026-08-06T17:58:29.593995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:29.354721Z","title":"Fine- grained classification with noisy labels,","venue":null,"work_id":"21fb4644-32f3-4d64-8e2c-2f052af717c2","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.229461Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:37a102fac19c964ac8119d1fd54ec2ffdf11dbfe567960c43e9cc25aeef569c3","observation_id":"2442420a-b9a0-4e2b-b1bd-7616f5e06064","resolution":{"observed_at":"2026-08-06T17:58:29.438406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:29.175394Z","title":"Meta label cor rection for noisy label learning,","venue":null,"work_id":"8848f431-a7b8-4b4d-81ec-b086a5ad1139","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.313346Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:03bae997857597c8eeef36283e863332c72740d71af9190f271920a3f5e017df","observation_id":"6744755d-8cdc-453d-877c-969bb0132c0f","resolution":{"observed_at":"2026-08-06T17:58:29.263504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.982376Z","title":"NoiseBox: To wards more efficient and effective learning with noisy labels,","venue":null,"work_id":"c753e2b5-8c94-44e3-bd35-67da962649df","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.389555Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:c85d3da1d3f24fc6f8e7b2f07cc7a1f3286c0687d754b97c94d10fb335e39766","observation_id":"8fae0b64-f92b-442d-8fe4-93155d446b1a","resolution":{"observed_at":"2026-08-06T17:58:29.060511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.784764Z","title":"Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality as sessment,","venue":null,"work_id":"bdf71b64-c989-43d8-9e29-139eb5bb1ecb","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.501221Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:41955ab324ce4be6b50733c8329ae85a5195a076f3789326701ae4d97543b0b1","observation_id":"04cf12b4-882f-48ad-a641-3ae4b929b99a","resolution":{"observed_at":"2026-08-06T17:58:28.865600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.610804Z","title":"Improving medical images classification with label noise using dual-uncertainty estimation,","venue":null,"work_id":"537cf0a2-e45a-40ef-b5cf-6d14e2ffd9d7","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.583813Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:8e5556abebd976a6d1527a9d927cfe4dd27624554f14e2459aa48c9f756bdba1","observation_id":"90bc4032-ba04-4f22-bc4d-e02a10c7960c","resolution":{"observed_at":"2026-08-06T17:58:28.693191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.413176Z","title":"A fundus image classification framework for learning with noisy labels,","venue":null,"work_id":"fe1b633b-7bdf-4025-8ec0-bec6773b60ae","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.677955Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:2e15de5dea1aee2b97051f97e84248cc2cf2b6a6a6d56aea3c799e048a0e7ea2","observation_id":"e29ec5fc-a1b9-4c5a-b6ae-c05cf7c82825","resolution":{"observed_at":"2026-08-06T17:58:28.548737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.212935Z","title":"Robust stocha stic neural ensemble learning with noisy labels for thoracic disease cl assification,","venue":null,"work_id":"93a17ab9-457d-4bac-a452-aa03aca0ce3c","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.751987Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:761fbb32796f35326806e7970f119d25b14cf4ab67fc454e1990d68eb3b70b7b","observation_id":"c88084f4-8487-4297-b44d-51db4dfddf79","resolution":{"observed_at":"2026-08-06T17:58:28.290306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:28.051842Z","title":"Federated optimization in heterogeneous networks ,","venue":null,"work_id":"e88cc367-990e-405f-a517-6cd5631a50de","year":2020},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.798854Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:4de6f403e8f42ca191a22bff9e5906e784efece1442af50def861a10c5d1f817","observation_id":"1dc78205-03f8-4bfb-b5b2-5f293d843d15","resolution":{"observed_at":"2026-08-06T17:58:28.123173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:27.746090Z","title":"Robust federated learning: The case of affine distribution shifts,","venue":null,"work_id":"1d069e48-6b13-47e4-baa1-4021d5dbe998","year":2020},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.908752Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:05795ba3514f1ce1900cbb6b29eddcb48312f54e8ced6a547ebbe2daa3e335fe","observation_id":"b22985e3-7151-4c0e-bb74-bff10655fea7","resolution":{"observed_at":"2026-08-06T17:58:27.869115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:27.577019Z","title":"Fed-DR-Filte r: Using global data representation to reduce the impact of noisy lab els on the performance of federated learning,","venue":null,"work_id":"960feb79-d4ec-447f-be76-d2daf05395fa","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:19.991375Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:aad4bd451cd1cf7876e314369ecf4357e83c5a95d203c3da5747f7c8516314d1","observation_id":"c88c328d-9df1-4954-91b1-3bca733fe03c","resolution":{"observed_at":"2026-08-06T17:58:27.664371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:27.451426Z","title":"Fedcorr: Multi- stage federated learning for label noise correction,","venue":null,"work_id":"84c32bda-ffcb-44e2-b6f1-6390d07b799b","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.064289Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:788bfb1a4c908a33dcb23a0684b17654389121d6515ac5826999aff128310b95","observation_id":"2ee3a7b1-1bd4-4cab-88c9-5d106867bbf7","resolution":{"observed_at":"2026-08-06T17:58:27.496786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:27.323689Z","title":"Robust federated learning with noisy and heterogeneous clients,","venue":null,"work_id":"23b97dd8-238f-4fdf-8844-5d0ded2678b0","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.146902Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:744f191086f91ee67a0ce5b035266d977fe5c3988f4d4ec78b3d542d46698ab6","observation_id":"7d991305-5ac2-4c2a-b68d-dea11bfb1a24","resolution":{"observed_at":"2026-08-06T17:58:27.389677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:27.088817Z","title":"Towards federated learning against noisy labels via local self-regularization,","venue":null,"work_id":"dd3a6ebb-af45-4bda-ad43-f9ec26e63472","year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.226367Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:24d61a42041a75f63f14d01872444c8685bcf321350f2da63a8e0a4896a35c39","observation_id":"f6c04197-f46c-4daa-a3db-5a083cb5fa38","resolution":{"observed_at":"2026-08-06T17:58:27.181468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:26.924410Z","title":"Curriculum- Based Federated Learning for Machine Fault Diagnosis With Noisy L abels,","venue":null,"work_id":"e290132c-5632-4c57-a203-670d12501644","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.310222Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:2f13849f1c5689c64392fd620d2f60710648b839f6c274ccbc32f0d32e95c5c2","observation_id":"22a106e8-a254-45f0-9afc-9ee6c86d37e6","resolution":{"observed_at":"2026-08-06T17:58:26.999329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.33068","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:58:23.974724Z","title":"Federated data quality assessment approach: robust learn ing with mixed label noise,","venue":null,"work_id":"0e9ac223-0a03-41f0-b2df-6de59a85516b","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.364226Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:81c44f4c5ee110e5b1ff5c6a31e077370304cb34047a2935dcc6baf56286f348","observation_id":"c038eb6b-6f51-4ba5-9d1f-f86b9e8c0907","resolution":{"observed_at":"2026-08-06T17:58:24.080389Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11650","last_updated":"2025-02-22T06:52:04Z","snapshot_observed_at":"2026-07-06T15:44:42.776459Z","submitted_at":"2023-06-20T16:18:14Z","title":"FedNoisy: Federated Noisy Label Learning Benchmark","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11650","snapshot_observed_at":"2026-08-06T17:58:20.433666Z","title":"F ed- Noisy: Federated noisy label learning benchmark,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.433666Z"},"links":{"cited_paper":"/paper/2306.11650","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:3454054cd162f180d806d575a7778741a3148678f1a50296131f93bec72057ec","observation_id":"77bd4d24-1684-4960-b8b4-06d726d78b3d","resolution":{"observed_at":"2026-08-06T17:58:20.433666Z","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":"2026-07-06T13:12:01.486619Z","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":"2026-08-06T17:58:20.517112Z","title":"Fednoil: A simple two-level sampling method for federated learning with nois y labels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.517112Z"},"links":{"cited_paper":"/paper/2205.10110","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:70abd5dd1705ed8aa224368e175f2ca46b48f02aa281fd84ee5a5a038d2f886b","observation_id":"cb0d244a-e77f-4725-883e-50173590ea53","resolution":{"observed_at":"2026-08-06T17:58:20.517112Z","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":"2026-08-06T17:58:26.745293Z","title":"Medical federated l earning with joint graph purification for noisy label learning,","venue":null,"work_id":"0ca8b3a4-50c2-40e3-a66b-36e275cafe50","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.610261Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:dbf3f21d5644eca102c53723b2f3f522876cf7d40df516dafc811528145297df","observation_id":"dd640ded-98b6-4d80-baa7-0d168d66f5c3","resolution":{"observed_at":"2026-08-06T17:58:26.840768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:26.547876Z","title":"Intelligent hand ling of noise in federated learning with co-training for enhanced diagnost ic precision,","venue":null,"work_id":"2d664da8-dfd1-45b8-a199-18e79926502c","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.756159Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:41c97be4f5b18c53f7762932036a40f9b18f04eb59c0f9207177ebb1545f6102","observation_id":"7d81b3dd-e3e7-4896-a9c5-8243d7ad1dcd","resolution":{"observed_at":"2026-08-06T17:58:26.650405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:26.379082Z","title":"Improving speaker verifi cation with noise-aware label ensembling and sample selection: Le arning and correcting noisy speaker labels,","venue":null,"work_id":"ce6f5a1d-820e-49a4-af87-1b8c6e415aca","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:20.887347Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:58862d746f73bd6c1fe2c5f7aecd21cada66b73c36676d9015bc70347ea887f9","observation_id":"52bac696-a3f4-4bfe-8f5b-ca18e3790f76","resolution":{"observed_at":"2026-08-06T17:58:26.467273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:26.267605Z","title":"Permuter, J","venue":null,"work_id":"f1100f85-dbbd-4b68-b91b-8b5665d11067","year":2006},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.041517Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:dc63d8aa8b8a67ffa3cbe44c7682ca9d4ca83f55472bb782cd54d83c55fc3817","observation_id":"d66a38a7-72f1-4382-98d4-2cb1043bb303","resolution":{"observed_at":"2026-08-06T17:58:26.319536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.00925","last_updated":"2020-12-02T01:49:47Z","snapshot_observed_at":"2026-08-07T16:52:53.177977Z","submitted_at":"2020-12-02T01:49:47Z","title":"SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2012.00925","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.00925","snapshot_observed_at":"2026-08-06T17:58:23.544951Z","title":"SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning","venue":"cs.LG","work_id":"eaeb0444-cc7e-49f1-8d22-1b9291503f6e","year":2020},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.182574Z"},"links":{"cited_paper":"/paper/2012.00925","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:c9c021517c14e8095a69d5f8d9244ec6a4bff69af7862c3848a567335d4c7847","observation_id":"94344605-319c-449a-8603-23c2bf493bfa","resolution":{"observed_at":"2026-08-06T17:58:23.668480Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:26.030314Z","title":null,"venue":null,"work_id":"66023a28-2696-40b5-8d43-974c8e75dd25","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.306492Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:92f3f1cf57803f3a930f348e440dd5b19b109e36bb60aa93be88beb18a0065db","observation_id":"5f275a00-00cf-42c3-bd71-ff3a72ade1f0","resolution":{"observed_at":"2026-08-06T17:58:26.115506Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:25.896335Z","title":"Lienen, C","venue":null,"work_id":"6b95f4d7-1fbc-4c5e-a43c-49a4e7acf300","year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.464102Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:19b0cb4fb39d4f70444f0c1eae2703a3d56a8f1276376525812340b44f3a9af0","observation_id":"c5743028-beaa-4870-ad38-d10e833fcfda","resolution":{"observed_at":"2026-08-06T17:58:25.955501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:25.744662Z","title":"Lienen and E","venue":null,"work_id":"46087130-250e-4fac-b8d3-dbf159561816","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.538913Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:067e2816fbbaa49b43824368dcc655f28a60f3a4ba3575f58462f35854178955","observation_id":"ecba41d0-8570-4d60-b0ce-2f7f089d4697","resolution":{"observed_at":"2026-08-06T17:58:25.826408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05043","last_updated":"2025-01-25T09:18:33Z","snapshot_observed_at":"2026-07-06T17:13:38.033348Z","submitted_at":"2024-01-10T10:04:49Z","title":"CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks","version":3},"cited_work":{"arxiv_id":"2401.05043","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.05043","snapshot_observed_at":"2026-08-06T17:58:23.221909Z","title":"CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks","venue":"cs.LG","work_id":"b12dd454-e3df-4b76-a8bd-a803a3e885a0","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.647029Z"},"links":{"cited_paper":"/paper/2401.05043","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:a71c978bf98095d8cc4329a890f0af97d93e631161575104421f4d4ae02e0ebb","observation_id":"fe9c8e6c-9f73-46a6-b8f1-eca26d506cb8","resolution":{"observed_at":"2026-08-06T17:58:23.390926Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:25.559741Z","title":"Possibility theory, probabili ty theory and multiple-valued logics: A clarification,","venue":null,"work_id":"6e1413c2-d61e-4fbc-80b8-44797f10fd96","year":2001},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.744637Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:e04b406141a4887d8b0b2f67a1120224f13d0c47ca15b3c2f0ce80e7fd3268c3","observation_id":"b7bc9ed8-1044-4a23-8e89-21f2e2d32367","resolution":{"observed_at":"2026-08-06T17:58:25.687797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.18147","last_updated":"2024-10-24T07:25:40Z","snapshot_observed_at":"2026-07-06T19:22:58.341345Z","submitted_at":"2024-09-25T02:41:58Z","title":"SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss","version":3},"cited_work":{"arxiv_id":"2409.18147","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.18147","snapshot_observed_at":"2026-08-06T17:58:22.929552Z","title":"SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss","venue":"cs.CV","work_id":"440a1786-8598-4ad6-93e2-d3544085cd67","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.835297Z"},"links":{"cited_paper":"/paper/2409.18147","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:cede0206a10d9b3d65d2c8af8e1e30e7b4ee17d88e06b840f4eb5c411d164946","observation_id":"b9dcb525-95f0-42c6-a8d8-2be9af299b70","resolution":{"observed_at":"2026-08-06T17:58:23.059205Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00957","last_updated":"2024-10-23T15:40:23Z","snapshot_observed_at":"2026-07-06T17:23:59.319572Z","submitted_at":"2024-02-01T19:25:58Z","title":"Credal Learning Theory","version":4},"cited_work":{"arxiv_id":"2402.00957","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.00957","snapshot_observed_at":"2026-08-06T17:58:22.695300Z","title":"Credal Learning Theory","venue":"cs.LG","work_id":"aeef8938-0eb2-49b5-b512-807148251741","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:21.954258Z"},"links":{"cited_paper":"/paper/2402.00957","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:5e5e62c6d7410d8e73c42ace4493b64ccd66e181ded92c0dd1fe42f080d90c9e","observation_id":"420f992c-74be-4b84-85fa-85e98455903d","resolution":{"observed_at":"2026-08-06T17:58:22.749107Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:25.340596Z","title":"Kvasir-Capsule, a video capsule endoscopy dataset,","venue":null,"work_id":"878d9a98-c283-439f-bf99-0dd6211b8cf7","year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.058915Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:b9227fcaa7ab7779d64effea80f7ead1bb68a93b0ee6691f6901c4218e4f9093","observation_id":"115a614c-e6db-4ff0-9f9f-6e0ddc12b6ef","resolution":{"observed_at":"2026-08-06T17:58:25.447013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:25.058481Z","title":"A benchmark of oc ular disease intelligent recognition: One shot for multi-disea se detection,","venue":null,"work_id":"2501f384-b2ee-46ab-8034-dbaebb1b63d7","year":2020},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.141043Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:6a46daa0b231187a9fa147d612b0614599b2bee95fbf8f14deda54a2b35b4134","observation_id":"33678951-cc0d-4417-9bd9-6d8effbdbb8d","resolution":{"observed_at":"2026-08-06T17:58:25.192187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:22.219928Z","title":"Hard sample aware noise robust learning for histopathology image class ification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.219928Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:5390b1c9d6924bccb21c6b44cc60a2a36a8f235518f07c1356f7f7e02c01cd2f","observation_id":"5ad8d9c7-fd6f-4fb8-8e9e-fd55ef428285","resolution":{"observed_at":"2026-08-06T17:58:22.219928Z","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":"2026-08-06T17:58:24.789088Z","title":"L VM-Med: Learning large-scale self-supervised vision models for medical imaging via second-order graph ma tching,","venue":null,"work_id":"45c0fde5-7195-43a7-9479-2ad4dd103c56","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.321244Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:9b13930d03c79aabbb42bf03d99cae4f83d5d593207d87003509c8f2d153e72a","observation_id":"baee7072-21b6-4a49-8465-f57d9e4b3535","resolution":{"observed_at":"2026-08-06T17:58:24.921425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:58:24.538864Z","title":"Ji, et al","venue":null,"work_id":"dd46e639-1ac5-44f7-a64c-b46eec374719","year":2024},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.421719Z"},"links":{"citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:697ca1adc7c6a5a6d84dd4915296f1f2d7d7c64e1b534d1562d373e9c0c8aaa3","observation_id":"985e4c8d-6821-47d9-9816-8911ffc7e8d5","resolution":{"observed_at":"2026-08-06T17:58:24.641595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05230","last_updated":"2023-08-01T10:18:08Z","snapshot_observed_at":"2026-07-06T15:24:57.443346Z","submitted_at":"2023-05-09T07:45:55Z","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05230","snapshot_observed_at":"2026-08-06T17:58:22.560816Z","title":"FedNoRo: Towards noise-robust fe derated learning by addressing class imbalance and label noise hete rogeneity,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:22.560816Z"},"links":{"cited_paper":"/paper/2305.05230","citing_paper":"/paper/2507.10611"},"observation_digest":"sha256:6fdd99e79c6df4ca0a03a0b08c38b85d1b71792bdd6fe784414bb94270c3f789","observation_id":"f5408f91-bd3a-4322-99c2-919299ca0298","resolution":{"observed_at":"2026-08-06T17:58:22.560816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.10611","last_updated":"2025-07-13T08:51:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T17:50:50.481870Z","submitted_at":"2025-07-13T08:51:51Z","title":"FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":5,"verified_fuzzy":38},"total_outbound_references":53},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2507.10611."}