{"as_of":"2026-08-09T17:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:272a42b5a3973acf72a71d55c9d5df381bcf7b0d33dbde01ea40a39e78899358","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T21:10:23.304210Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.04890/citation-record","integrity":"/paper/2502.04890/integrity","json":"/paper/2502.04890/citation-record.json","paper":"/paper/2502.04890"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:10:23.125410Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.125410Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:ec14ec1efe6b7d75046f8ca807290fe687ae2a67fd387793f082da1d1ec85a24","observation_id":"743ef33f-5c29-4d39-9c6e-7e0330d29e07","resolution":{"observed_at":"2026-08-08T21:10:23.125410Z","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":"2026-08-08T21:10:23.130865Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.130865Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:82b46202d343dfe6dd2eac1abc0c272c102705913c9b61be125cab88e22395cc","observation_id":"a68fd5c2-635c-4f59-9d76-47f367c1a451","resolution":{"observed_at":"2026-08-08T21:10:23.130865Z","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":"2026-08-08T21:10:23.853607Z","title":null,"venue":null,"work_id":"0b430bd1-5da4-4b30-8b34-1a82978d6a3c","year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.136073Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:c269bbb98e26dbc35718acf60b59bc9f3ed3f6bba42f2782135912b4a6afd211","observation_id":"97114717-c50a-4dc5-9599-d7a47bee886a","resolution":{"observed_at":"2026-08-08T21:10:23.858201Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.839753Z","title":null,"venue":null,"work_id":"674bba07-364c-4dbe-a44b-bd50eaa694fe","year":2024},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.141222Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:aaaf979db8654324ef85f35003916b491200467473adcae6c206e8b35ef45d28","observation_id":"bc48267a-f473-4a3f-9cbc-25033c5d7cc9","resolution":{"observed_at":"2026-08-08T21:10:23.844186Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.01772","last_updated":"2023-02-03T14:30:25Z","snapshot_observed_at":"2026-07-06T14:48:07.182076Z","submitted_at":"2023-02-03T14:30:25Z","title":"Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity","version":1},"cited_work":{"arxiv_id":"2302.01772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.01772","snapshot_observed_at":"2026-08-08T21:10:23.440936Z","title":"Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity","venue":"cs.LG","work_id":"93a2b406-7140-4df5-9daf-e384192e07a8","year":2023},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.145940Z"},"links":{"cited_paper":"/paper/2302.01772","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:38072d3db72b4ce879987b9c4bb3e89fe85af3feb2985511c691d15baccd6049","observation_id":"9d5db1dd-34c3-4161-be33-c244a4e38d30","resolution":{"observed_at":"2026-08-08T21:10:23.445885Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.826087Z","title":null,"venue":null,"work_id":"7b0508fe-b5d7-4498-95ed-de1e2272f1b5","year":2019},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.150925Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:146cc0d2d204ca6d9fe8cfdc3575abd5657a5b4ed228c3eceb1f0fa8f9417696","observation_id":"7d0af5d4-220a-4e03-97b2-dc68852361bc","resolution":{"observed_at":"2026-08-08T21:10:23.830484Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.808854Z","title":"M.; Guerraoui, R.; and Stainer, J","venue":null,"work_id":"21b2c2a0-9b8d-41a0-bf96-3827e4cb22d3","year":2017},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.155506Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:26fb143f82ea1a28ed6fe73ec62368b17bd7691b99d103aae2280ac6ab6d694f","observation_id":"87045aa7-da7d-4344-972f-e42a60568c27","resolution":{"observed_at":"2026-08-08T21:10:23.814832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.793422Z","title":"M.; Guerraoui, R.; Maurer, A","venue":null,"work_id":"ad2604af-f27f-4e85-9ff0-5ff759c2b474","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.159906Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:d5fd2150b2057bd5215b2f3e4bdfd45fef51be572fd6735a10981c373cedb1b5","observation_id":"83c36886-52f9-488c-93d2-cd2383d98d49","resolution":{"observed_at":"2026-08-08T21:10:23.798037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-08T21:10:23.164727Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.164727Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:edb7b0371353a382e04892abf60a7b82b42657fc72d5100b55e40ad12cd20473","observation_id":"3ddb3446-6c30-447a-bdba-0aaf90ad41dc","resolution":{"observed_at":"2026-08-08T21:10:23.164727Z","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":"2026-08-08T21:10:23.777474Z","title":"M.; Farhadkhani, S.; Guerraoui, R.; Guirguis, A.; Hoang, L.-N.; and Rouault, S","venue":null,"work_id":"3c6dc7a1-0bc2-48f4-af70-ba75eec3c8d8","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.169344Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:8d3ef1cf26750f435ab05d7d873afe638e74f9641a6bed09775f3854b07b0b5c","observation_id":"bb95a78d-6003-4079-8aa7-185216294ea4","resolution":{"observed_at":"2026-08-08T21:10:23.782866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.763163Z","title":null,"venue":null,"work_id":"cd1cf538-9a80-4b3e-91a9-18b108a3b0ba","year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.174403Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:a2ab33b077af5a2db9b9a45c795ee475e5717564c8096f59de70b8446eb21eb4","observation_id":"a711b0f0-211a-44f0-bf50-fab4207e64a4","resolution":{"observed_at":"2026-08-08T21:10:23.767628Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.748491Z","title":null,"venue":null,"work_id":"dc609fe4-963d-4d8d-a34f-48e45bc85372","year":2022},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.181882Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:1a29917edc29de99d525334971c3da893b33004705d61da31749bd23e4e7d5cf","observation_id":"e27f2d32-61bd-4380-87e0-c647c71f636a","resolution":{"observed_at":"2026-08-08T21:10:23.753328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.733506Z","title":null,"venue":null,"work_id":"1a58e2be-4350-4ec4-9cb7-5b4599d43a71","year":2018},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.186841Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:440c342bb1de139423aa3ca071af648266b636502e6226c0518e1bd4b1a51402","observation_id":"df4c8baf-262a-4d73-a04f-5cf59465ba5a","resolution":{"observed_at":"2026-08-08T21:10:23.738462Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.191260Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.191260Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:2419ece80bf0c43ecdf498fa05e60e529fd0772bcfd11dc3b7161cd2a135a81f","observation_id":"9d5dd842-87cb-46c9-bf85-186d8a1510ce","resolution":{"observed_at":"2026-08-08T21:10:23.191260Z","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":"2026-08-08T21:10:23.710565Z","title":"P.; He, L.; and Jaggi, M","venue":null,"work_id":"46f69814-67a3-4c5f-a7ac-b0ce7e74884a","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.195548Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:a3e339fb1e71e92f913a0d71c650b28e93831dbc51a976bee10e4e86bdc28564","observation_id":"73443219-c4ef-4ea1-9106-5620e160efd3","resolution":{"observed_at":"2026-08-08T21:10:23.715101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.695728Z","title":"P.; He, L.; and Jaggi, M","venue":null,"work_id":"20f93112-f369-4b42-a3d4-db426884adc1","year":2022},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.200004Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:7ac3e759fadfd5bb34c436d450fcc1163685c05d7b673606517ae4826b96f1fd","observation_id":"2a84392e-d07f-414b-a616-0d27f6cacb95","resolution":{"observed_at":"2026-08-08T21:10:23.700348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.681039Z","title":"P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A","venue":null,"work_id":"3ca370c3-5e80-4377-9ba2-9012cd0a9ce0","year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.204330Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:6f8116012098903b13df27281d4870e12b4ec92e220f6706d95275dac9b6c37f","observation_id":"edb113ca-bd67-4247-b4e9-e53b34559ccc","resolution":{"observed_at":"2026-08-08T21:10:23.685862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.666850Z","title":null,"venue":null,"work_id":"16af8d92-37f0-470c-8a00-3e59fdaac9dc","year":2002},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.208630Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:42ad15ecd56148c0c0f9f21279f6b4a372609821ca8e254bcbde3480a8f92a9f","observation_id":"464d8413-a9bd-49b4-908c-c6d8706d1fe3","resolution":{"observed_at":"2026-08-08T21:10:23.671438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.213486Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.213486Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:c0dd4ed4a277191806e04fc26084da841bc110bf316d418cf8948c5860acee9f","observation_id":"654cb7a5-eac0-4f04-b785-e46d467bcaf4","resolution":{"observed_at":"2026-08-08T21:10:23.213486Z","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":"2026-08-08T21:10:23.643893Z","title":null,"venue":null,"work_id":"a83eab39-a4cb-4895-a991-7f7766b73e9c","year":2017},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.217944Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:6fd480daa06d88d6cd3b1f9091a4f6c8948eab6cb020d3d1dcefc631855d4d3e","observation_id":"9f1b901b-ea49-4919-a1be-adb74e3e60c6","resolution":{"observed_at":"2026-08-08T21:10:23.648339Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.02079","last_updated":"2021-10-28T15:22:21Z","snapshot_observed_at":"2026-08-09T02:19:17.266233Z","submitted_at":"2021-02-03T14:29:09Z","title":"Federated Learning on Non-IID Data Silos: An Experimental Study","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.02079","snapshot_observed_at":"2026-08-08T21:10:23.222453Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.222453Z"},"links":{"cited_paper":"/paper/2102.02079","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:8e1fc7bc4678240726cccaca01823a611ea778d0278197d69b7ba761957d50c6","observation_id":"8b8d13bf-f0f0-42e7-b914-af0e18f0f4cb","resolution":{"observed_at":"2026-08-08T21:10:23.222453Z","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":"2026-08-08T21:10:23.226964Z","title":"K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.226964Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:b4783d3fae8a410eaed7c1eb700854d052d78b824216ab68690472c949bdf0c7","observation_id":"888cdc95-1a64-40ea-872f-34fc3e8b1d93","resolution":{"observed_at":"2026-08-08T21:10:23.226964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02189","last_updated":"2020-06-25T06:45:52Z","snapshot_observed_at":"2026-08-06T15:26:37.394053Z","submitted_at":"2019-07-04T02:04:56Z","title":"On the Convergence of FedAvg on Non-IID Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02189","snapshot_observed_at":"2026-08-08T21:10:23.231404Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.231404Z"},"links":{"cited_paper":"/paper/1907.02189","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:00a915cd814d481014b2b4bf04e95129f1ebe429f9658090a69b8dea1043b28b","observation_id":"24c81ebb-777b-4d5f-9b6a-e6934abc7783","resolution":{"observed_at":"2026-08-08T21:10:23.231404Z","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":"2026-08-08T21:10:23.620494Z","title":null,"venue":null,"work_id":"cbd3cca6-61b5-4f77-a4fa-82143db2ecf2","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.235972Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:83f2a5e0877958ac0c1460dd86551ab45794a98ba34c982dac2be930b1c2cffc","observation_id":"dcf17e23-099a-4cda-a9b8-4d56f53c77c3","resolution":{"observed_at":"2026-08-08T21:10:23.624899Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.606041Z","title":null,"venue":null,"work_id":"ffa3b65c-df85-4ffd-b49e-0825713409e0","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.240821Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:e71d6145500ee9b6fcb13815d24670732e66c4057f600337e6645054253b5e3e","observation_id":"f308baac-c446-43c8-b680-8aba7a63d3da","resolution":{"observed_at":"2026-08-08T21:10:23.610495Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.245019Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.245019Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:be2f1dc4b169b144c125aec14a71923075dfb78f9d99100d6fdbcbdd411a747c","observation_id":"f47e1ec2-9761-42b5-b5b2-f9276de9dab8","resolution":{"observed_at":"2026-08-08T21:10:23.245019Z","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":"2026-08-08T21:10:23.581548Z","title":"S.; Mccabe, G","venue":null,"work_id":"820b89ca-3c41-48be-b883-12d300d770f4","year":2009},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.249357Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:825ec0ed29fe89ea4e8353533369cdd684cfcd7075edc1560c7fe09756941cce","observation_id":"d520c51a-81ec-41aa-9d3d-f14c7a75aa1e","resolution":{"observed_at":"2026-08-08T21:10:23.586403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15458","last_updated":"2024-06-04T02:36:14Z","snapshot_observed_at":"2026-07-06T18:19:17.573250Z","submitted_at":"2024-05-24T11:33:58Z","title":"FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler","version":2},"cited_work":{"arxiv_id":"2405.15458","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.15458","snapshot_observed_at":"2026-08-08T21:10:23.371950Z","title":"FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler","venue":"cs.LG","work_id":"19a85e0f-2cbc-499d-acd7-0918b979f949","year":2024},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.253695Z"},"links":{"cited_paper":"/paper/2405.15458","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:23629768ac7e5388cd472c4959b33c84140a851815c884dcd0194e0dd00fcb48","observation_id":"8a8cacfa-7dbe-49ad-8b1e-d9a4d9d9d243","resolution":{"observed_at":"2026-08-08T21:10:23.379777Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.13445","last_updated":"2022-01-17T05:25:59Z","snapshot_observed_at":"2026-07-06T08:47:53.346676Z","submitted_at":"2019-12-31T17:24:41Z","title":"Robust Aggregation for Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.13445","snapshot_observed_at":"2026-08-08T21:10:23.258314Z","title":"M.; and Harchaoui, Z","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.258314Z"},"links":{"cited_paper":"/paper/1912.13445","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:0fd375f455668f618b8052a9778d77d2c94fd9b0acbf41f92ac8efe435cb49ff","observation_id":"80fe7ec9-5c57-4b45-af37-d2ffcb078654","resolution":{"observed_at":"2026-08-08T21:10:23.258314Z","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":"2026-08-08T21:10:23.567624Z","title":"T.; and Saul, L","venue":null,"work_id":"6995104d-c425-4114-8dfb-5b82fbcb8047","year":2000},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.262872Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:1388cbcca7d80892c73fbc310fa208fc146f1e7e34c3ab16e34f846eeab1c68e","observation_id":"790079e1-dec1-4f37-b7fd-629d44119d8c","resolution":{"observed_at":"2026-08-08T21:10:23.571989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.266901Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.266901Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:3341b70db5843c8287f64dc9330053161dd6b5252ae5d54aebad56e3251580c6","observation_id":"68ab238d-46ae-45ef-abd5-0809495df7b7","resolution":{"observed_at":"2026-08-08T21:10:23.266901Z","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":"2026-08-08T21:10:23.543159Z","title":null,"venue":null,"work_id":"5d38ffc8-ee4d-420a-ac63-ab8ce2e6229b","year":2021},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.271146Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:65a935234c0137a56ce6f3ec84d93a1350a586f06eb25bcfd2f745a3f5084242","observation_id":"e0d30448-5785-4ed3-a294-1ce839f033f9","resolution":{"observed_at":"2026-08-08T21:10:23.547514Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.276275Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.276275Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:65014bc0865c3cee4cc98622e147b5e7846432ae3dbd0c0e7dfca7ef8da6bded","observation_id":"932360c3-3c83-4f9d-ac44-0ef457e5821b","resolution":{"observed_at":"2026-08-08T21:10:23.276275Z","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":"2026-08-08T21:10:23.517207Z","title":"E.; Patel, K","venue":null,"work_id":"b128a606-dd0d-40ef-a9d9-929604e19bf9","year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.280874Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:72f60c59152b630b604713c94936ec89fd6fe26d5dc6cb9145399781d542ff17","observation_id":"9f502ef3-c5eb-4e50-b474-f728f7af8b51","resolution":{"observed_at":"2026-08-08T21:10:23.522752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.501792Z","title":null,"venue":null,"work_id":"3b4a4260-4426-4497-aabd-7ef2a3951d2b","year":2020},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.285319Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:9b79eabe356343f80897391f5e91d3da8b2fce54baff64c050b50bc687922fa6","observation_id":"001da8ce-2f37-46f6-98bd-88aadc65be56","resolution":{"observed_at":"2026-08-08T21:10:23.506438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.487352Z","title":null,"venue":null,"work_id":"e3a2c73a-40a8-4779-a2c3-f113382e3d14","year":2024},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.290515Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:b60f40797fafb909514193023e6c159d9fb86f68530bfb50341fe39370050872","observation_id":"dd16966d-cbdb-4c52-abfa-194c3414bf2d","resolution":{"observed_at":"2026-08-08T21:10:23.492073Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T21:10:23.471024Z","title":null,"venue":null,"work_id":"4e060819-bd9c-4c4b-b521-9c917925472b","year":2018},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.294887Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:eab4c7d3ed805dac4b5768214035ade70239eaaa779f4cc43fe98abe4ebd4ad4","observation_id":"ec2f43dc-1697-40be-bb08-85559d1139fa","resolution":{"observed_at":"2026-08-08T21:10:23.475594Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.06629","last_updated":"2018-11-16T07:57:46Z","snapshot_observed_at":"2026-07-06T06:50:48.184959Z","submitted_at":"2018-07-17T19:14:17Z","title":"Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.06629","snapshot_observed_at":"2026-08-08T21:10:23.299468Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.299468Z"},"links":{"cited_paper":"/paper/1807.06629","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:6f0454bf413cdac694c4719b15425b16e23359609e36d32111ad4388462bb35a","observation_id":"43b62278-1a89-4453-9b6d-c4b6220df9f9","resolution":{"observed_at":"2026-08-08T21:10:23.299468Z","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":"2026-08-08T21:10:23.456222Z","title":null,"venue":null,"work_id":"87b5deb3-63d8-4262-a037-ec4bcfd650dc","year":2019},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.304210Z"},"links":{"citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:b7f0e2ec32d1709ee0fe9bad262fa8caf2b8bf076c10a23ce2bf66c262d7f510","observation_id":"d5191c32-94bb-4faa-b7e4-97df2b4d9d68","resolution":{"observed_at":"2026-08-08T21:10:23.460887Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T21:03:40.797953Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":2,"verified_fuzzy":9},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2502.04890."}