{"as_of":"2026-08-16T06:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:90000e549d05e94aaf5774b5e3e65c1b9432e2882627297df620a71606149520","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:51:51.527168Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.10605/citation-record","integrity":"/paper/2412.10605/integrity","json":"/paper/2412.10605/citation-record.json","paper":"/paper/2412.10605"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.557165Z","title":"Communication-efficient learning of d eep networks from decentralized data","venue":null,"work_id":"f447a095-3fd0-4248-941e-521a2bd8b411","year":null},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.235872Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:93bb0083f7486cb209cf83435cd40432a6595d314557200a26563c54095f6784","observation_id":"18053937-0fe3-4c40-9cc8-4f9eae7409ba","resolution":{"observed_at":"2026-08-11T15:51:52.566035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.527179Z","title":"Federated learning for connected a nd au- tomated vehicles: A survey of existing approaches and chall enges","venue":null,"work_id":"64d61818-1f6c-4815-b5e5-e18ff4bc41a5","year":2023},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.245214Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:c133dea95b9fb02fb51ae667da8d68f8b76c2a5a3bc19126d2089823b69440c8","observation_id":"2770a5fe-1511-455f-9bde-6b447b005fa2","resolution":{"observed_at":"2026-08-11T15:51:52.536406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.505069Z","title":"Federated learning for health- care: Systematic review and architecture proposal","venue":null,"work_id":"8547051d-2f55-4d22-bdfc-ec8d2c8eb9b8","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.250203Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:28f4b0106f9ca817a272eb4673139d2adc2ff824f5803667c3aafbebf9ab1ac7","observation_id":"72263f93-1c58-4cff-ae94-e4c0d6253f35","resolution":{"observed_at":"2026-08-11T15:51:52.511189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.479249Z","title":null,"venue":null,"work_id":"aabe6258-d280-4354-a663-8abf27d6ad19","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.254608Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:4f9f2581fd0650eee5c30c6bf8600cdb29f225907214f48e686d88c66680164f","observation_id":"bda5ee7f-0c94-46c1-aa5e-4352402d874f","resolution":{"observed_at":"2026-08-11T15:51:52.486409Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.457510Z","title":"How to backdoor federated learning","venue":null,"work_id":"2d073059-55f2-4291-85aa-c5e3c73e453f","year":2020},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.259662Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:76862ff825da450eb7cffc85b9bc15bb323d64fa1e5e4d250dfb2600c02a28cc","observation_id":"9439ddf6-cb8b-4e12-b10c-721eaa5407fb","resolution":{"observed_at":"2026-08-11T15:51:52.463644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.430556Z","title":"Privacy and robustness in federa ted learning: Attacks and defenses","venue":null,"work_id":"a50061fe-450e-480c-aea7-4d4176eb199f","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.264133Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:76398ea3090a89c47b06c9fe91dd01c7d871cfae36a1ceb3dc20389a30371099","observation_id":"47178035-1103-4040-ad35-700ab9d9f192","resolution":{"observed_at":"2026-08-11T15:51:52.436807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.410304Z","title":"Federated learning with dif- ferential privacy: Algorithms and performance analysis","venue":null,"work_id":"04335b52-fd7b-43c8-b37a-1265dc02f3c4","year":2020},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.268408Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:0840254eccf71fb591f0980ee12ccebf2e37eb5c6af3b78fa7b74fc3f68f6d9c","observation_id":"0c71bda3-08e8-4cee-abbe-4607f82e384e","resolution":{"observed_at":"2026-08-11T15:51:52.415276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.374064Z","title":"{FLAME}: Taming backdoors in federated learning","venue":null,"work_id":"9390ffec-2e45-4abf-935e-8db7571c1703","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.274118Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:7ff197833f53440e2cbbf24c4df1e2228dd7655b1328debbd5c31e86522b0b6a","observation_id":"9031c247-8302-4d79-8824-ba791b15e9bb","resolution":{"observed_at":"2026-08-11T15:51:52.380144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14240","last_updated":"2024-09-20T11:24:52Z","snapshot_observed_at":"2026-08-12T22:57:25.789397Z","submitted_at":"2024-08-26T12:54:00Z","title":"Celtibero: Robust Layered Aggregation for Federated Learning","version":2},"cited_work":{"arxiv_id":"2408.14240","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.14240","snapshot_observed_at":"2026-08-11T15:51:51.724540Z","title":"Celtibero: Robust Layered Aggregation for Federated Learning","venue":"cs.CR","work_id":"361a23d3-82b2-4002-a47a-237e08d9e1f1","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.286046Z"},"links":{"cited_paper":"/paper/2408.14240","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:af84fa81da81355e7168856b846247a90cd304c33bcf681deaa3a076d81cd92f","observation_id":"8bc52ed6-e497-4da8-8296-fe0488eb86ce","resolution":{"observed_at":"2026-08-11T15:51:51.732277Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.352855Z","title":"Neurotoxin: Durable backdoors in federated learning","venue":null,"work_id":"219fe4ae-fc02-4530-ad23-27c93ac8f0d0","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.292130Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:424a7c1af760896f8240036a7ed8971c32444397e0c5a3332d57f4b85177442e","observation_id":"8c90f003-f3fc-4f49-ae32-3ac76ef63ee7","resolution":{"observed_at":"2026-08-11T15:51:52.359024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.335482Z","title":"Differentially private learning with adaptive cli pping","venue":null,"work_id":"eae163ab-1106-45aa-9b77-57a470d48747","year":2021},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.306060Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:efe7a5ff78673a6cc907bb897e77117b04587e82cca6812ac7922689afc675ad","observation_id":"a13a3e26-901f-4e3d-96d9-bd188aece5de","resolution":{"observed_at":"2026-08-11T15:51:52.340864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.316899Z","title":null,"venue":null,"work_id":"246cbcf6-d9b3-4577-bb23-115ce624cb62","year":2020},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.310292Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:b02afd63c00157db634bb4dd7188388bceeb9dac0963fba3efd1b966a513c631","observation_id":"52427825-7912-4a4c-8007-f95823d5f372","resolution":{"observed_at":"2026-08-11T15:51:52.323311Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.298292Z","title":"Poisoning with cerberus: Stealthy an d colluded backdoor attack against federated learning","venue":null,"work_id":"53949d4a-bc51-4c12-b06c-a495480767f5","year":2023},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.318570Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:b6728292e9f9b364d6901bbf4d14e2aa3a359d1c69b32b0e04a492328227b952","observation_id":"8283e63c-d1c5-401b-a835-9719b364fb68","resolution":{"observed_at":"2026-08-11T15:51:52.303387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-11T15:51:51.324408Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.324408Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:9652a681eb0904b86c76b4c0ba43e6fe6d43ccce4775588983d148fd7f46d08d","observation_id":"2d216eed-00ba-4f97-ab7b-799ae69eac8c","resolution":{"observed_at":"2026-08-11T15:51:51.324408Z","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":"2026-08-11T15:51:52.281284Z","title":"Towards deep learning models res istant to adversarial attacks","venue":null,"work_id":"48eb2596-8614-4559-80c1-6563e6efc355","year":2017},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.332430Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:85bedfc38b62abb453be311a67c6fc7a9cdd46ec47c44a63eb0a67e658f8335a","observation_id":"ef6b967d-b241-430f-b303-431fc1ef8e4d","resolution":{"observed_at":"2026-08-11T15:51:52.286564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.262088Z","title":"A sur- vey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies","venue":null,"work_id":"3e9d4f1c-79a2-4368-aa73-5cbf487e701a","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.340556Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:728074128e502b05ed967dbb5cc3875966a69d16d5a2496c2ede6ca3bd461d20","observation_id":"aa074b75-2205-4a31-bdba-c16bde948649","resolution":{"observed_at":"2026-08-11T15:51:52.268901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.240822Z","title":"L atent back- door attacks on deep neural networks","venue":null,"work_id":"83b063b6-d311-4f40-98b2-3eea17dcb7b8","year":2019},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.350648Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:ce14b89889362b6c5c718c895a6e9d007d58d453dbd17eb45f4241c1cff3e387","observation_id":"3f89eee3-a292-41ca-a25c-7ffed4c8b2b9","resolution":{"observed_at":"2026-08-11T15:51:52.248261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.225111Z","title":"Input-aware dynamic backd oor attack","venue":null,"work_id":"d75c143f-bf7e-47ed-8c9d-02a42a1d964e","year":2020},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.356480Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:4f50cd4c8843abac6259bcaaaae312fc30a66e7b26c2d265edee53ca8c91feed","observation_id":"8680f1e1-2076-4ed3-902a-2b7aae752efb","resolution":{"observed_at":"2026-08-11T15:51:52.230241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.361578Z","title":"The mnist database of handwritten digits","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.361578Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:100b9c7467457d5f154196de2eb3b94db935a630ca1e41fda94278e69fa735c6","observation_id":"f71df71e-00bb-40dd-959f-10a95c3ede32","resolution":{"observed_at":"2026-08-11T15:51:51.361578Z","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":"2026-08-11T15:51:52.189678Z","title":"Dba: Dist ributed back- door attacks against federated learning","venue":null,"work_id":"3df6b39b-18da-4d90-a7a9-2ec31138335a","year":2019},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.368517Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:5e2c2ef0b57ff9b8d69f612a0d16e6b23ddbee53de29c2257387d3dc3fce56c7","observation_id":"e20ff5c8-d9e7-4e53-b2e5-b7682220e2f8","resolution":{"observed_at":"2026-08-11T15:51:52.204386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.169891Z","title":"On the dirichlet distribution","venue":null,"work_id":"31f1d0ad-c152-49f9-9a91-9352681da889","year":2016},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.375760Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:58e66bf35a964d7d31cd363e589dbff0d3ac875b562476279cf46e82b6448eb9","observation_id":"097927ab-5fe9-4548-9656-b59c28b3dfa2","resolution":{"observed_at":"2026-08-11T15:51:52.176888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.152146Z","title":"Median-krum : A joint distance-statistical based byzantine-robust algorithm i n federated learning","venue":null,"work_id":"64cc8770-ef79-4483-9704-b5e8ddd49fac","year":2023},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.383445Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:42d23f9da6d103226ba1a1789729d65fb83a39a9f85960a51a5ec0be8c36b7bc","observation_id":"3fb9d882-5c69-4eb7-b774-c5e06eb3f0ba","resolution":{"observed_at":"2026-08-11T15:51:52.157960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.133445Z","title":"The limitations of federated learning in sybil settings","venue":null,"work_id":"b8d8f0e6-31ce-45bc-a422-f6e0fedebf9f","year":2020},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.393199Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:cab8408b346a73191accf0078e0c90c6d35b440b70b09cbfc7355892d6036945","observation_id":"bd21765e-5395-43e2-b50f-b872d27434f6","resolution":{"observed_at":"2026-08-11T15:51:52.138085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.108660Z","title":"Lfighter: Defending against the lab el-flipping attack in federated learning","venue":null,"work_id":"744342be-5c57-4b41-87e4-049e2b5b4b9a","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.398842Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:99c4f58727460de09fbef2717cc176092c0537c6817f60b477b220c3b631596f","observation_id":"19a7f46b-92a0-4ecd-b160-1a4bbaf001ca","resolution":{"observed_at":"2026-08-11T15:51:52.118533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.073769Z","title":"Roseagg: Robust defense against targeted collusion attacks in feder ated learning","venue":null,"work_id":"70a1ea78-465c-4329-94a1-d579d32bd2e2","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.404740Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:17390db5f0d30017703b38dbb43fd09832fb45011c9e56123b140e724ca9cda5","observation_id":"e51193ab-c2cf-4e68-929b-ea38db294f53","resolution":{"observed_at":"2026-08-11T15:51:52.080257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.07963","last_updated":"2019-12-02T19:00:11Z","snapshot_observed_at":"2026-08-09T08:16:29.597021Z","submitted_at":"2019-11-18T21:25:03Z","title":"Can You Really Backdoor Federated Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.07963","snapshot_observed_at":"2026-08-11T15:51:51.410822Z","title":"Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.410822Z"},"links":{"cited_paper":"/paper/1911.07963","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:a2fda32e3b5f562cf1c151b68e343de86826311c629f28a0d01ec6bb9082ed13","observation_id":"e1b3baea-2616-4922-b2d2-1204b64a2f38","resolution":{"observed_at":"2026-08-11T15:51:51.410822Z","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":"2026-08-11T15:51:52.050426Z","title":"Vppfl: A verifiable privacy-preserving federated learning s cheme against poisoning attacks","venue":null,"work_id":"b52532ae-3264-4477-9c5d-91ff0379085f","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.415769Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:83e6a02db3e0a3bdc57ee570b09d586d555c4b2fcb01736dfc6582636be28be0","observation_id":"f2a3a83e-1c09-461b-b537-1e25dd03a559","resolution":{"observed_at":"2026-08-11T15:51:52.057811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:52.016609Z","title":"Machine learning with adversaries: Byzantine tol erant gradient descent","venue":null,"work_id":"7741a719-a616-4621-b24b-84356088ec3f","year":2017},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.421530Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:acb17407e957b5ffd1f9304ecfde5ab588dc81da0d14c8945b0ed2c74187f2c1","observation_id":"ce0d785e-6c31-4d6d-8ad0-9575e7bda84f","resolution":{"observed_at":"2026-08-11T15:51:52.024702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.983733Z","title":"Fl- defender: Com- bating targeted attacks in federated learning","venue":null,"work_id":"40365899-b0f9-4d13-8e26-c00e33593e3b","year":2023},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.428592Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:8a2ae7805b2e0da61db50bb1409aa40713f17cd4ba73428127b01e33fb4abbc5","observation_id":"4d9e51d1-623b-4676-aff1-3bde8e2af7fd","resolution":{"observed_at":"2026-08-11T15:51:51.989146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.961428Z","title":"Shield-secure aggregation a gainst poisoning in hierarchical federated learning","venue":null,"work_id":"665d7a92-8399-4226-a34a-81fc79481c71","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.441406Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:24839284367c0ee78f52039d2e30c24c3ac092e2e22f1185a8b996f16869629f","observation_id":"2e18f081-ea0a-4686-ac44-a96445665666","resolution":{"observed_at":"2026-08-11T15:51:51.969366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.943334Z","title":"Cali- brating noise to sensitivity in private data analysis","venue":null,"work_id":"4ae96ed3-8400-4163-8f39-5ca2e77bc153","year":2006},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.447932Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:2d1f68d8888bea9b84517d989c1f97d68f2ae19c229d3d352c56f9db5a73e184","observation_id":"b9b21132-571e-4e3d-8363-526fcb685e03","resolution":{"observed_at":"2026-08-11T15:51:51.948496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.917876Z","title":"Lomar: A local defense against poisoning attack on federate d learning","venue":null,"work_id":"bedc13b1-03d9-4b76-8756-b110adbb651f","year":2021},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.455535Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:f700a54f6a5d558e2533d6e808296c2639950da12bae1463195495dec4b405e4","observation_id":"a03f3dc9-ce15-49b9-98d8-50b836a64758","resolution":{"observed_at":"2026-08-11T15:51:51.924934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.13995","last_updated":"2022-04-12T01:56:47Z","snapshot_observed_at":"2026-07-06T10:28:06.533127Z","submitted_at":"2020-12-27T18:43:39Z","title":"FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.13995","snapshot_observed_at":"2026-08-11T15:51:51.464270Z","title":"Fltrust: Byzantine-robust federated learning via trust bootstrapp ing","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.464270Z"},"links":{"cited_paper":"/paper/2012.13995","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:89823b82778a5ae9a30d6b6f2783608c32ecac91d32997da9721c6d05e3528f1","observation_id":"2921db73-baa6-42c6-b5e6-99c2f1557d9c","resolution":{"observed_at":"2026-08-11T15:51:51.464270Z","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":"2026-08-11T15:51:51.897798Z","title":"Baffle: Backdoor detection via feedback-based fe derated learn- ing","venue":null,"work_id":"49a26d86-92c5-434b-9113-2e4843ddc857","year":2021},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.470279Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:674f4d114321244d2689c441f2433269b9390b4871a201628f1063d5edb09e56","observation_id":"6668b156-843c-4d54-bb4c-dab9340a021d","resolution":{"observed_at":"2026-08-11T15:51:51.904372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.875390Z","title":"M aking federated learning robust to adversarial attacks by learni ng data and model association","venue":null,"work_id":"f1c40955-fb2c-4891-b4a0-ec5e2207e2e6","year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.478826Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:565d7ed93fd9ba9be5c31877e923d0fffdf6a28e29345400cb6313c397888082","observation_id":"07abf539-1732-4b65-b7a2-45dc3920c8f4","resolution":{"observed_at":"2026-08-11T15:51:51.881331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.846400Z","title":"Crowdguard: Federated backdoor de tection in federated learning","venue":null,"work_id":"8d99a507-e883-4399-a8b9-9d2b5c406b72","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.484218Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:db978a01f943c6b6bac0e79d8a2e31fd8572e8fde4c3243d86e1a095587bf4e4","observation_id":"e1f3ec46-44e0-445c-ab2b-832f1b423a13","resolution":{"observed_at":"2026-08-11T15:51:51.861905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.815628Z","title":"Toward resource-efficient federa ted learning in mobile edge computing","venue":null,"work_id":"457b9c07-5781-4072-8ab3-5fb8ac8cf720","year":2021},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.488884Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:6269a94b2f82e2151516212baa720a88cad9302b78a943f928d1dc321ee66101","observation_id":"ee7fb8d0-649c-4516-acda-b3d35d76f92c","resolution":{"observed_at":"2026-08-11T15:51:51.821484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01767","last_updated":"2021-01-14T19:53:17Z","snapshot_observed_at":"2026-08-11T06:49:34.066608Z","submitted_at":"2020-10-28T18:39:28Z","title":"Mitigating Backdoor Attacks in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01767","snapshot_observed_at":"2026-08-11T15:51:51.496191Z","title":"Mit igating back- door attacks in federated learning","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.496191Z"},"links":{"cited_paper":"/paper/2011.01767","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:3045472412391238c6da54bcaa425d418c1fee100d699ae0e673e69fc04f1bdb","observation_id":"82cae3e1-9d27-4748-829f-dd9a80fd841b","resolution":{"observed_at":"2026-08-11T15:51:51.496191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.09441","last_updated":"2022-01-24T03:56:20Z","snapshot_observed_at":"2026-08-13T16:54:27.438825Z","submitted_at":"2022-01-24T03:56:20Z","title":"Federated Unlearning with Knowledge Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.09441","snapshot_observed_at":"2026-08-11T15:51:51.502127Z","title":"Federated unl earning with knowledge distillation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.502127Z"},"links":{"cited_paper":"/paper/2201.09441","citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:c3ccfddf47bd050705c03080fc1fd608d30e1102c1ddf556d2f2043428981da1","observation_id":"8ac011eb-a322-457f-9478-eaad729b1226","resolution":{"observed_at":"2026-08-11T15:51:51.502127Z","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":"2026-08-11T15:51:51.788743Z","title":"Adaptive machine unlearning","venue":null,"work_id":"d1acaa17-6c86-47e6-adda-ab0dabefdf15","year":2021},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.512777Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:5c94823817e9133365ec12dfe2ad3a64f56b416bb7d3e5822d8b5e03b868ca0f","observation_id":"0cd1b5be-9de4-4dfa-9f50-40c05b31474a","resolution":{"observed_at":"2026-08-11T15:51:51.794691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.767612Z","title":"Sars: A personalized federated learning framework towards fairne ss and robustness against backdoor attacks","venue":null,"work_id":"c4197fcb-daf1-4ee9-95fe-784fa0b14311","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.521480Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:28b62f179537660d7e934baeaa23637bcabd1b72ec9e923fbf3bb90b7b0587e3","observation_id":"a042577c-57cc-485b-89d9-773fede710d6","resolution":{"observed_at":"2026-08-11T15:51:51.772868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:51:51.745466Z","title":"One-shot backdoor removal for federated lear ning","venue":null,"work_id":"ee622b25-bc2c-488f-8a15-b98e74c5126e","year":2024},"citing_paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T15:51:51.527168Z"},"links":{"citing_paper":"/paper/2412.10605"},"observation_digest":"sha256:2b5b3f0b67ad69f96a54e8d118fbc87bcc0d079eeec20affff397fbc51305bc2","observation_id":"dff2039c-b477-4096-8df6-95afef0d0d9f","resolution":{"observed_at":"2026-08-11T15:51:51.751395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.10605","last_updated":"2024-12-20T09:59:28Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-13T11:24:03.113266Z","submitted_at":"2024-12-13T23:17:10Z","title":"Client-Side Patching against Backdoor Attacks in Federated Learning"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":33},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.10605."}