{"as_of":"2026-08-21T03:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91760dfd92b537aea522f7d261166ea694e39fc74062e51904c27a1e433d877d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:58:55.138267Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T15:16:04.818899Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-12T19:05:12.334643Z","title":"Towards efficient and certified recovery from poisoning attacks in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11039","last_updated":"2024-11-17T11:08:49Z","snapshot_observed_at":"2026-08-18T12:49:13.104898Z","submitted_at":"2024-11-17T11:08:49Z","title":"FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:05:12.334643Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2411.11039"},"observation_digest":"sha256:a1540d317d7be0fc45e9dd59376c4046be33ac34e0032ad61fd2ad2e309bf465","observation_id":"31a7096c-1dda-4cbf-8d6b-3c745a3c5f92","resolution":{"observed_at":"2026-08-12T19:05:12.334643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-12T19:04:58.513589Z","title":"Toward s efficient and certified recovery from poisoning attacks in federated l earning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11044","last_updated":"2024-11-17T11:45:15Z","snapshot_observed_at":"2026-08-15T22:34:56.656025Z","submitted_at":"2024-11-17T11:45:15Z","title":"Efficient Federated Unlearning with Adaptive Differential Privacy Preservation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:04:58.513589Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2411.11044"},"observation_digest":"sha256:9e81832feb604f32dae6fc4f9066ce2bedef9a7fa5c176a2408b7a88358391ee","observation_id":"19d9d318-a316-476a-9721-95ae9ca3f4ba","resolution":{"observed_at":"2026-08-12T19:04:58.513589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-12T10:35:58.249994Z","title":"Towards efficient and certified recovery from poisoning attacks in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00126","last_updated":"2025-03-10T00:54:33Z","snapshot_observed_at":"2026-08-20T00:19:10.833199Z","submitted_at":"2024-11-28T12:52:48Z","title":"Streamlined Federated Unlearning: Unite as One to Be Highly Efficient","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T10:35:58.249994Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2412.00126"},"observation_digest":"sha256:ac8098cbd4f6f5a855e1bc2b6df085f4005a4cd525e0d717e868b1cfe5ad7bf3","observation_id":"b130dee9-a3a9-4762-b84d-262d9f938404","resolution":{"observed_at":"2026-08-12T10:35:58.249994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-10T15:46:20.115745Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13683","last_updated":"2025-01-23T14:10:02Z","snapshot_observed_at":"2026-08-18T18:04:10.799214Z","submitted_at":"2025-01-23T14:10:02Z","title":"Unlearning Clients, Features and Samples in Vertical Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T15:46:20.115745Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2501.13683"},"observation_digest":"sha256:289cdf50840751250495b2f4beb7323f11e0ab6012717cfcd4599f0cbd57da84","observation_id":"40567561-7c61-42d3-bdf1-0382961a9c20","resolution":{"observed_at":"2026-08-10T15:46:20.115745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-15T23:58:55.138267Z","title":"Towards efficient and certified recovery from poisoning attacks in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03388","last_updated":"2025-05-06T10:10:18Z","snapshot_observed_at":"2026-08-17T23:13:36.874256Z","submitted_at":"2025-05-06T10:10:18Z","title":"Memory-Efficient Distributed Unlearning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T23:58:55.138267Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2505.03388"},"observation_digest":"sha256:9a980c881d51f472e43d987ecefe96fd23c8ebda008440b5a8d4ebbfbe3a73be","observation_id":"030e7e15-dac3-44d2-823d-ee019d2419e1","resolution":{"observed_at":"2026-08-15T23:58:55.138267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-08-15T19:48:10.267255Z","title":"Towards efficient and certified recovery from poisoning attacks in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15112","last_updated":"2025-06-18T03:30:07Z","snapshot_observed_at":"2026-08-20T00:19:11.760487Z","submitted_at":"2025-06-18T03:30:07Z","title":"PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T19:48:10.267255Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2506.15112"},"observation_digest":"sha256:70a0f6223f871858ba5804a557d71fef8c76083d165b5b76ceb1d9fa6c7df1e0","observation_id":"988b894c-c0a3-4882-b0e2-b31d50a2f623","resolution":{"observed_at":"2026-08-15T19:48:10.267255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":"2401.08216","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.08216","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards efficient and certified recovery from poisoning attacks in federated learning","venue":null,"work_id":"ed635007-c79c-4d4a-867d-805da3452d07","year":2024},"citing_paper":{"arxiv_id":"2605.00169","last_updated":"2026-04-30T19:41:26Z","snapshot_observed_at":"2026-08-10T23:56:05.566921Z","submitted_at":"2026-04-30T19:41:26Z","title":"Network Digital Untwinning: Towards Backward Optimization of Digital Twins","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-09T20:27:24.371255Z"},"links":{"cited_paper":"/paper/2401.08216","citing_paper":"/paper/2605.00169"},"observation_digest":"sha256:178957c41e2225e6b8b2ff19cf4e57d4bcf79c5188a48f22efba9ee109497d11","observation_id":"3f5d8f43-0779-42f7-b9fc-3acd37c9961e","resolution":{"observed_at":"2026-05-11T15:16:04.822656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.08216/citation-record","integrity":"/paper/2401.08216/integrity","json":"/paper/2401.08216/citation-record.json","paper":"/paper/2401.08216"},"outbound":[],"paper":{"arxiv_id":"2401.08216","last_updated":"2024-01-19T05:31:07Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-18T16:28:46.280382Z","submitted_at":"2024-01-16T09:02:34Z","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2401.08216."}