{"as_of":"2026-08-08T01:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c2e04a7743b6673c3e74e95bf5bf6ae506cacefccfc4bcfd7d20ab1e7d251ec","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:44:20.351591Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"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/2505.21219/citation-record","integrity":"/paper/2505.21219/integrity","json":"/paper/2505.21219/citation-record.json","paper":"/paper/2505.21219"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:44:28.176344Z","title":"Badr, Mohamed M","venue":null,"work_id":"3bd720e2-38ec-44be-8720-eca9ffd4ebe3","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.043965Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:83720376e94b32298d28d9a7ec0a5042d42e88e72066fefb0fe1906147588db2","observation_id":"3b8229aa-0797-4476-96e6-10379c463d0a","resolution":{"observed_at":"2026-08-07T13:44:28.296028Z","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":"2026-08-07T13:44:27.995904Z","title":"Diverse client selection for federated learning: Submodularity and convergence anal- ysis","venue":null,"work_id":"6f02d3fc-c1bf-4b01-94ba-863461f32285","year":2021},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.092939Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:f261c9d66aa22f51e5d7a1012059301a1f0c3c716b2f6db1a81e238d90d45c9a","observation_id":"10eae771-bd7d-40f0-91c2-f9f10e5c5653","resolution":{"observed_at":"2026-08-07T13:44:28.089662Z","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":"1902.01046","last_updated":"2019-03-22T20:25:57Z","snapshot_observed_at":"2026-07-06T07:30:52.835880Z","submitted_at":"2019-02-04T06:27:41Z","title":"Towards Federated Learning at Scale: System Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01046","snapshot_observed_at":"2026-08-07T13:44:15.167321Z","title":"McMahan, Timon Overveldt, David Petrou, Daniel Ramage, and Jason Roselander","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.167321Z"},"links":{"cited_paper":"/paper/1902.01046","citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:75105be883dd6f7f45d09b2426d176473e9c406f7a5ad53b41d8102319d34dcb","observation_id":"db83b00f-e949-4cb8-b90d-2fa15aede043","resolution":{"observed_at":"2026-08-07T13:44:15.167321Z","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":"2026-08-07T13:44:27.802649Z","title":"Evaluating feder- ated learning for intrusion detection in internet of things: Review and challenges.Computer Networks, 203:108661, 2022","venue":null,"work_id":"dcf0b5be-d194-42b4-9269-3770defc85ea","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.252206Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:b90a9d9d352d944dae5606c606429815e882b89fda7bd49bbadd4bb9e59452c6","observation_id":"3116f438-cb5f-439c-b8e0-2faa4312c9b9","resolution":{"observed_at":"2026-08-07T13:44:27.921867Z","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":"2026-08-07T13:44:27.494731Z","title":"A credible and fair federated learning framework based on blockchain.IEEE Transactions on Artificial Intelligence, 6(2):301–316, February 2025","venue":null,"work_id":"90ffe0be-5818-4969-a905-bafd5494c2c7","year":2025},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.329634Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:1864e4ce9b04f4a6ffb44039b3648f9ebad0b2fb6a40ff539df8521d48122caa","observation_id":"dbf4cf5a-4c33-4801-bdb3-b17dc4f48b9a","resolution":{"observed_at":"2026-08-07T13:44:27.630809Z","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":"2026-08-07T13:44:27.276783Z","title":"Emnist: Extend- ing mnist to handwritten letters","venue":null,"work_id":"bc9d6585-3436-49a7-942b-e92d4dd81cca","year":2017},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.450888Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:0105ad04aa140f506bab899b8c5aeb1c96204f2a9a1eccf6f370a568b535727a","observation_id":"3fe30d61-7af0-40a1-afc5-8923ba3aa4e4","resolution":{"observed_at":"2026-08-07T13:44:27.377170Z","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":"2026-08-07T13:44:27.051301Z","title":"Local model poisoning attacks to byzantine-robust federated learning","venue":null,"work_id":"762430f7-ec94-443e-8436-6925aebe68e3","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.534629Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:609e9122c4f52414a258121844c55e55f3d029cede435e3c7684a11b55884e15","observation_id":"38b04737-f04d-438f-a56b-b4cd3291d113","resolution":{"observed_at":"2026-08-07T13:44:27.164907Z","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":"2026-08-07T13:44:26.754822Z","title":"Clustered sampling: Low- variance and improved representativity for clients selection in federated learning","venue":null,"work_id":"f244e188-3eff-488e-a7ca-bd2ab2dbc2d7","year":2021},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.688674Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:e6e34bd69df146922b695a1477ca1b76b8cf464068d1dd5d85a8865a4a371a8f","observation_id":"3f84f02a-d336-40a3-b7d4-6df614cf91a2","resolution":{"observed_at":"2026-08-07T13:44:26.883262Z","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":"2026-08-07T13:44:26.515527Z","title":"Data shapley: Equitable valuation of data for machine learn- ing","venue":null,"work_id":"5c652ee4-d29d-4290-b992-82c952c9f1bb","year":2019},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.821454Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:d60a01d11b65110fb0ecae62c360ca06d92081b980d3b6b5311e863255537de1","observation_id":"1a96e181-efb6-4fe6-bd70-2e294a6d67dc","resolution":{"observed_at":"2026-08-07T13:44:26.620895Z","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":"2026-08-07T13:44:26.217589Z","title":"Victoria Luzón","venue":null,"work_id":"90abdec6-568a-4102-865d-23cade840c17","year":2025},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:15.995964Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:5fec19607e97e23b99c33251d20db7d65b77b07dbe0788b684407b0d770932c4","observation_id":"f02c36b6-3af3-4176-b093-e5d1a80828a8","resolution":{"observed_at":"2026-08-07T13:44:26.306157Z","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":"2026-08-07T13:44:26.002814Z","title":"Promoting collaboration in cross-silo federated learning: Challenges and opportunities.IEEE Communications Magazine, 62(4):82–88, April 2024","venue":null,"work_id":"b71cfa45-d41b-4413-ac1b-1e1d524d1b7f","year":2024},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.168986Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:63300ee3a773b6dbaa81c0e1a6954276a68834b7c0dbf5152ea6b61baf979f2a","observation_id":"9b471569-b842-46eb-8837-0fcc78ce51f8","resolution":{"observed_at":"2026-08-07T13:44:26.093534Z","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":"2026-08-07T13:44:25.740775Z","title":"Towards understanding biased client selection in federated learning","venue":null,"work_id":"11b84a9b-ee18-4566-aa11-85413e6d7f28","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.301932Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:b97e8bbe051e4bd746be387930b6faf6d4c4e3b6b26d94227133909da52ec82e","observation_id":"0f7f734d-1d22-412a-b6fa-e8de1c489ed3","resolution":{"observed_at":"2026-08-07T13:44:25.857636Z","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":"2026-08-07T13:44:25.501374Z","title":"Optimal user selection for high-performance and stabilized energy-efficient feder- ated learning platforms.Electronics, 9(9):1359, 2020","venue":null,"work_id":"f880c89e-bc3d-401e-800a-1a32422d7f7e","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.420971Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:7ac00a8c7fb47c9fa9990c169118385ee9b77ab63608b7b231c3b6b34565ae37","observation_id":"a6e1728a-2818-4aa8-97dc-9abd2865550c","resolution":{"observed_at":"2026-08-07T13:44:25.633828Z","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":"2026-08-07T13:44:25.260403Z","title":"Toward an automated auction framework for wireless federated learning services market.IEEE Transactions on Mobile Computing, 20(10):3034–3048, 2020","venue":null,"work_id":"db855c7f-2967-4be8-b7cc-d0601b5443e7","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.516109Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:e03bb2eff578c5bbce23e1b3c093e75cd8091788957fd6e72ade3f154d517192","observation_id":"3e4e7ba9-3068-4a76-99d4-f32211930a91","resolution":{"observed_at":"2026-08-07T13:44:25.355025Z","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":"2026-08-07T13:44:25.230400Z","title":"Prospect theory: An analysis of decision under risks","venue":null,"work_id":"7bbb8415-9d4d-47b5-86ba-4a1e6887537d","year":1979},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.643066Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:b375ce087874edad5ea701c143f5b4b79f8b434f52e8ad088a817e5056e0c2ff","observation_id":"e357a779-afcd-4193-aa7f-cf74b9ff2c4d","resolution":{"observed_at":"2026-08-07T13:44:25.251644Z","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":"2026-08-07T13:44:25.067441Z","title":null,"venue":null,"work_id":"02b456bf-b43f-441e-a2cb-d4f673750434","year":2019},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.710301Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:fcb1122839619c7d3a358f542d2ffd2a527f99f3c9bb406d6dc50c62d23a303f","observation_id":"d5d29fa2-2aa9-426e-b834-5f83e59bba9c","resolution":{"observed_at":"2026-08-07T13:44:25.128735Z","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":"2026-08-07T13:44:24.837456Z","title":"Khan, Shashi Raj Pandey, Nguyen H","venue":null,"work_id":"5f233225-be18-4695-9bbc-156088de709a","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.836328Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:1ffca54a8db15565de2045cb7a13706cd0c2b05d0a1598d5871afcfcca3bbf7b","observation_id":"0e1be66d-216f-4cbf-82ee-cdeea3ae9573","resolution":{"observed_at":"2026-08-07T13:44:24.965381Z","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":"1610.02527","last_updated":"2016-10-08T13:25:15Z","snapshot_observed_at":"2026-07-06T05:13:51.685562Z","submitted_at":"2016-10-08T13:25:15Z","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02527","snapshot_observed_at":"2026-08-07T13:44:16.968866Z","title":"Federated optimization: Distributed machine learning for on-device intelligence.arXiv preprint arXiv:1610.02527, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:16.968866Z"},"links":{"cited_paper":"/paper/1610.02527","citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:6d81eaadeb99ec84abb26676154ede841a55d8ebfb8566ec0bf9f274d04400db","observation_id":"bad19ed7-3af5-47c0-8bd8-0262e80dacd9","resolution":{"observed_at":"2026-08-07T13:44:16.968866Z","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":"2026-08-07T13:44:17.073076Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.073076Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:054745691385194a94fea918aec415a80cc370b36e02c4396961646c89d81c00","observation_id":"f1f8474f-270c-433a-a676-02150c1a54fd","resolution":{"observed_at":"2026-08-07T13:44:17.073076Z","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":"2026-08-07T13:44:24.661201Z","title":"Data distribution-aware online client selection algorithm for federated learning in heterogeneous networks.IEEE Transac- tions on Vehicular Technology, 72(1):1127–1136, 2023","venue":null,"work_id":"aadd789c-9f1b-4755-9709-884589d0834e","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.185430Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:8ea4530cef4cdb9a4eee376fd149b5bd4541619ec13a227e65f129be3b3b624e","observation_id":"03c47588-cd71-4cf3-a36e-c79c443b3bd3","resolution":{"observed_at":"2026-08-07T13:44:24.740553Z","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":"2026-08-07T13:44:17.330417Z","title":"A review of applications in federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.330417Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:3cdd26936edf837ebde87e85fe9292cac1493e494691a35617380c67f58e2c5f","observation_id":"a4939251-c3c0-47ad-901d-6f12f12d0265","resolution":{"observed_at":"2026-08-07T13:44:17.330417Z","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":"2026-08-07T13:44:24.469771Z","title":"On the conver- gence of fedavg on non-iid data","venue":null,"work_id":"80f1aeab-ad3d-4ed5-b389-c2a54660eea1","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.472120Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:f869ee29345e1dad5408e19208d4b10e7842e388d092b45f1c38594f765d90c9","observation_id":"dbbeabf5-ef13-4571-bbe4-de07ce750ce4","resolution":{"observed_at":"2026-08-07T13:44:24.569784Z","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":"2026-08-07T13:44:24.300735Z","title":"Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning.ACM Transactions on intel- ligent Systems and Technology (TIST), 13(4):1–21, 2022","venue":null,"work_id":"3e3f037c-7c67-4486-99f7-5bd5bf6ddea2","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.568331Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:d6868f8ecf4e89bc8a43cc6ac5aacc6af7fc554849ec6b15240bc4101e7a5d68","observation_id":"4b10cce9-22e3-4e1d-9b96-ffd34e0bbef3","resolution":{"observed_at":"2026-08-07T13:44:24.392748Z","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":"2026-08-07T13:44:24.165621Z","title":null,"venue":null,"work_id":"fdf9ad5d-cf7f-44ab-82dd-03b00c874b20","year":null},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.682821Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:9fa539ea993b8bef6a81fce010568e8d3c141c383abd529c8f7a70cbf5e61bf7","observation_id":"b922bb3b-b342-4fd2-b69f-94defd0751f6","resolution":{"observed_at":"2026-08-07T13:44:24.228502Z","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":"2026-08-07T13:44:23.907570Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":"fdc7a1a1-701b-4a30-b61e-d2c35e334524","year":2017},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.809437Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:750526a32039ca0b6fc7c861198d4cc77f4988dd9d21cd7594c1a4b589daf7c9","observation_id":"8e651ff1-9113-482b-831c-3b0dbfd44c5f","resolution":{"observed_at":"2026-08-07T13:44:24.061873Z","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":"2026-08-07T13:44:17.925594Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:17.925594Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:ede649fef08f1f0e68bd46f09c9a741512f2b8c9aeef69b689df12aea819100a","observation_id":"5870395c-ad15-4731-ab02-49ce0d347a19","resolution":{"observed_at":"2026-08-07T13:44:17.925594Z","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":"2026-08-07T13:44:23.535311Z","title":"Client selection for federated learning with heterogeneous resources in mobile edge","venue":null,"work_id":"f108d1f1-438d-4f6a-8d99-b10c399183db","year":2019},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.079295Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:c6e6c279458d87ef97c37bcf1d059d478a5b45d4b601dd4a6b94c5f57b6fb299","observation_id":"cc08a04d-7a46-4224-918b-677fca87299b","resolution":{"observed_at":"2026-08-07T13:44:23.740686Z","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":"2026-08-07T13:44:23.263394Z","title":"An incentive auction for heteroge- neous client selection in federated learning.IEEE Transactions on Mobile Computing, 22(10): 5733–5750, 2022","venue":null,"work_id":"6041e22f-9b3f-419b-876e-aed7f617eaff","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.225353Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:bc3b94128055a70e4a7467af1c47bc6b7c9dc1d498ce06908e363fce6246fd9e","observation_id":"0a21222d-abe1-49bf-9fc4-664d3ac7af50","resolution":{"observed_at":"2026-08-07T13:44:23.366949Z","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":"2026-08-07T13:44:23.050262Z","title":"Feddcs: A distributed client selection framework for cross device federated learning.Future Generation Computer Systems, 144: 24–36, July 2023","venue":null,"work_id":"53703481-8c0c-4fed-9cbb-5a44c4a69ddb","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.399903Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:005b4f9649ec193a924b4d64a0241bfeacde2e86ffe5ffe7d095101b25d94ec5","observation_id":"b35786e1-b292-4fc0-94e3-cc5254c7949a","resolution":{"observed_at":"2026-08-07T13:44:23.154389Z","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":"2026-08-07T13:44:22.738758Z","title":"High- quality model aggregation for blockchain-based federated learning via reputation-motivated task participation.IEEE Internet of Things Journal, 9(19):18378–18391, 2022","venue":null,"work_id":"6d2a63f8-a01e-4764-b141-6534d3cc084f","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.533082Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:f727ce51e40c61456fc0153aeb4dcdffaae27b60928246036157afc50ad9844c","observation_id":"8a392b29-79c7-42d7-bbb2-101bc8cd8b90","resolution":{"observed_at":"2026-08-07T13:44:22.858721Z","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":"2026-08-07T13:44:22.527401Z","title":"Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing, 26(4):2271–2311, 2023","venue":null,"work_id":"bec7a8fc-e615-4972-9da3-d040ff0cf4a5","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.707333Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:5c694cdf0629c755ea558dc9634ec805caa2b2e3a18cc914ebc993ec6df2bac2","observation_id":"83dbcd7a-7f38-4618-9930-d54a018e3874","resolution":{"observed_at":"2026-08-07T13:44:22.626605Z","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":"2026-08-07T13:44:22.333041Z","title":"Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges.Information Fusion, 90:148–173, 2023","venue":null,"work_id":"feb5a1ac-b669-4db9-ba63-c5d3d891cc61","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:18.922950Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:5b0655ba0af913c014a6f5510c3101f76a828121d63d6d61fb33bbf8955d6aa9","observation_id":"8ec9de21-3982-4bd7-823e-fa8ad8a71de7","resolution":{"observed_at":"2026-08-07T13:44:22.405494Z","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":"2026-08-07T13:44:22.154647Z","title":"Motivating workers in federated learning: A stackelberg game perspective.IEEE Networking Letters, 2(1):23–27, 2020","venue":null,"work_id":"e220db7b-ca2e-4a8a-896a-94dd59a312b1","year":2020},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.044291Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:8010efc2ea47beeb06dfd5f6164ed23d2a3b09b0aa387748b79c3ed5de37612a","observation_id":"26d85d47-2aa7-4521-b007-8ef448bb2823","resolution":{"observed_at":"2026-08-07T13:44:22.252292Z","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":"2026-08-07T13:44:21.931495Z","title":null,"venue":null,"work_id":"11f69670-8e37-4a39-906a-6f7c505828da","year":1953},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.175088Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:7455e72ac9e90b0eca319d7f0a12652be603263e71151096a16a60d4ac7fb836","observation_id":"aaa898f0-fdbe-4fd1-a2c4-482defc52466","resolution":{"observed_at":"2026-08-07T13:44:22.041569Z","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":"2026-08-07T13:44:21.728174Z","title":"Yang, Xijun Wang, Yan Zhang, and Tony Q","venue":null,"work_id":"c4c80f69-1993-4d22-bc63-f6a64bf74122","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.330999Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:31268771f71e7f5631f4e70e4a236d014fec16484503ea41c5ddd5f00048c069","observation_id":"157869b1-0c05-4c99-809e-ed4fd8fa9b54","resolution":{"observed_at":"2026-08-07T13:44:21.831608Z","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":"2026-08-07T13:44:21.534732Z","title":"Data poisoning attacks on federated machine learning.IEEE Internet of Things Journal, 9(13):11365–11375, 2022","venue":null,"work_id":"3931e7cc-0ee5-4c8d-ad95-7ce0e661e97f","year":2022},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.431727Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:a3191460875979d875f2c1e70184c5ed8dd602ef654981715c173e38519f77be","observation_id":"8598a2ef-9c3d-4671-9696-09ac56891e05","resolution":{"observed_at":"2026-08-07T13:44:21.608013Z","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":"2026-08-07T13:44:21.358714Z","title":"Incentive mechanism design for joint resource allocation in blockchain-based federated learning.IEEE Transactions on Parallel and Distributed Systems, 34(5):1536–1547, May 2023","venue":null,"work_id":"ebcdcd3b-2d56-4afc-8f25-eb8019774a7c","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.614179Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:30fd0e54016321e3c84b378cf475a358a259f81889775dd095240a9165017791","observation_id":"7bfc1bd4-87dc-4bd2-96a0-6016a3c442d9","resolution":{"observed_at":"2026-08-07T13:44:21.452614Z","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":"2026-08-07T13:44:21.168440Z","title":"A survey on federated learning: challenges and applications.International Journal of Machine Learning and Cybernetics, 14(2):513–535, 2023","venue":null,"work_id":"1fa9a42d-8fd4-438d-8ae9-07388569eaac","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.713758Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:87328a137ba78695fa4b301833bfeb122c2d8fc4d490d1d5539101add5f115d3","observation_id":"22bf0619-37f9-41a1-990e-da2943865949","resolution":{"observed_at":"2026-08-07T13:44:21.270933Z","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":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-07T13:44:19.855454Z","title":"Fashion-mnist: a novel image dataset for bench- marking machine learning algorithms.arXiv preprint arXiv:1708.07747, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:19.855454Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:8c8cd4f090365ec64df4ef9cbcc076dbf1334a45cae25c248438b5ef34b9731b","observation_id":"4aa060be-171b-43d4-b318-7c529869ee25","resolution":{"observed_at":"2026-08-07T13:44:19.855454Z","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":"2026-08-07T13:44:20.963098Z","title":"Jointly optimizing client selection and resource management in wireless federated learning for internet of things.IEEE Internet of Things Journal, 9(6):4385–4395, 2021","venue":null,"work_id":"9ea4bba4-f848-4514-ba90-2cdcbdec103a","year":2021},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:20.045184Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:721190b4ae6a9e08a5ed8c388f4c7723bfc7227834bf9699145294b6fdf74a55","observation_id":"0588fbf1-f13f-4439-a0a1-42e006e62263","resolution":{"observed_at":"2026-08-07T13:44:21.068555Z","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":"2026-08-07T13:44:20.769647Z","title":"Yu, and Christopher G","venue":null,"work_id":"24bae354-d069-42eb-a805-cd98d9456da3","year":2024},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:20.169333Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:7b35c1603ca47586b94aa418c73ab4a3b71938d33f7edd3697f15192813a08c1","observation_id":"9ef58fb7-b4f5-453a-8665-85d14acc2b28","resolution":{"observed_at":"2026-08-07T13:44:20.843934Z","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":"2026-08-07T13:44:20.626808Z","title":"A survey of trustworthy federated learning: Issues, solutions, and challenges.ACM Transactions on Intelligent Systems and Technology, 15(6):1–47, October 2024","venue":null,"work_id":"0f10e81e-b0cb-42c8-ad95-0c599939346f","year":2024},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:20.283180Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:0f15562cc1655fcb03101f0429c3eb60753f4c44a208e9d904eafb12ee3d94e6","observation_id":"9187fdd7-eeed-4513-93fe-aa39358c8a54","resolution":{"observed_at":"2026-08-07T13:44:20.699715Z","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":"2026-08-07T13:44:20.479426Z","title":null,"venue":null,"work_id":"7e20a477-f8a3-45ff-a8d6-6be25f811cde","year":2023},"citing_paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:20.351591Z"},"links":{"citing_paper":"/paper/2505.21219"},"observation_digest":"sha256:78d02a8f9c4106d3d2d8cb46f4f5fd60a707caa359890dcc8d75010aee5bb41f","observation_id":"dc3a73cb-16c1-46fe-a8d1-67cfb5949a28","resolution":{"observed_at":"2026-08-07T13:44:20.542995Z","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"}}],"paper":{"arxiv_id":"2505.21219","last_updated":"2025-05-27T14:06:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T13:30:38.328819Z","submitted_at":"2025-05-27T14:06:51Z","title":"Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":33},"total_outbound_references":43},"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 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2505.21219."}