{"as_of":"2026-08-16T07:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:84ab94f3fe4215760435bec21b91a9decc80ee59f569136f48cb36f2fdaf2001","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T18:43:49.010684Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2501.11054/citation-record","integrity":"/paper/2501.11054/integrity","json":"/paper/2501.11054/citation-record.json","paper":"/paper/2501.11054"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:43:49.975307Z","title":"Kulkarni, and H","venue":null,"work_id":"d6e7d6a7-711e-4ea1-8ee2-345013c9b647","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.767749Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:a1204bfd26bf755b344e432fa83f83469316f7fa79135318e26f609ce7620dd9","observation_id":"70912f07-897f-4ad9-baf2-89ded7c573ba","resolution":{"observed_at":"2026-08-10T18:43:49.980129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.959703Z","title":"Towards effective device-aware federated learning","venue":null,"work_id":"901b49b0-56f5-4f49-97c4-14bffa1dd2d4","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.773652Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:06ad9e7b1d172c6d913d05c533104dd00123e97baa1298a514f66ebe1767f0f4","observation_id":"4873f4f4-f4b5-42ce-b15c-beaa2b6f53b5","resolution":{"observed_at":"2026-08-10T18:43:49.964575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14390","last_updated":"2022-03-05T20:30:32Z","snapshot_observed_at":"2026-07-06T09:42:35.058716Z","submitted_at":"2020-07-28T17:59:07Z","title":"Flower: A Friendly Federated Learning Research Framework","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.14390","snapshot_observed_at":"2026-08-10T18:43:48.778581Z","title":"Flower: A friendly federated learning research framework","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.778581Z"},"links":{"cited_paper":"/paper/2007.14390","citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:cf86419f4f2aac1044aa8b7317941b4dbde2e532e05ab92f2b59ac27bce08793","observation_id":"d948563f-3c6d-4bd8-9537-c684208d1481","resolution":{"observed_at":"2026-08-10T18:43:48.778581Z","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":"2026-08-10T18:43:49.943583Z","title":"Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth","venue":null,"work_id":"00107e05-d8d5-4cb6-89c5-10cc379a7695","year":2017},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.786363Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:429a5d118d71cf683d605be64acba16b9b9029bbe4c6046b95dd694222160f2f","observation_id":"baa4c71e-e252-49f8-af64-69e7ec80c8af","resolution":{"observed_at":"2026-08-10T18:43:49.948803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.928382Z","title":"Federated learning with autotuned communication-efficient secure aggregation","venue":null,"work_id":"d055998c-91a3-41bb-ad65-163811180bf6","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.791660Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:a071153c79b4e443c8140501b0ec2b986afc27d0fba71f25e1f2834e7d6d1050","observation_id":"befbdd16-9733-4a5f-a8ae-58fcc2bcf356","resolution":{"observed_at":"2026-08-10T18:43:49.933345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.796621Z","title":"Random forests","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.796621Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:642a8f38359aa9e73642d0f6a77aad071b3ea31dcea587b3d94c28fe60afa4e5","observation_id":"2595a5c0-8a49-4b5e-ad18-6fb86ff583bc","resolution":{"observed_at":"2026-08-10T18:43:48.796621Z","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":"2026-08-10T18:43:49.900929Z","title":"Breunig, Hans-Peter Kriegel, Raymond T","venue":null,"work_id":"829f2bbe-b06b-441c-81da-4802d838c3a7","year":2000},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.801749Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:05624be6464444c1889708cae07d23402141988701d9c3e724f305a8a21c6bea","observation_id":"56adfba9-cc19-4997-9b8f-78b75be42c28","resolution":{"observed_at":"2026-08-10T18:43:49.906593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.884349Z","title":"MPAF: Model poisoning attacks to federated learning based on fake clients","venue":null,"work_id":"413591a6-06b7-49da-a2ee-9b61565dfa7d","year":2022},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.806202Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:4dbfd67ef3b7dbe12283a91b822810e1ab718eb358a242b17dfcee454d49da1e","observation_id":"f18a1986-119a-427b-993a-a7c9f7e4b099","resolution":{"observed_at":"2026-08-10T18:43:49.889149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.867945Z","title":"XGBoost: A scalable tree boosting sys- tem","venue":null,"work_id":"34cd038a-79a0-45bc-ad37-2ccd42c3401b","year":2016},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.810556Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:801d746ac1614ca248ccc2cc59df3b737d771ce50aeeffacc56d259a66f60507","observation_id":"00165767-8aa2-4a90-8bc2-9c5d366e4a50","resolution":{"observed_at":"2026-08-10T18:43:49.873206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-10T18:43:48.815075Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.815075Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:52012662dab7bfb3f9b9d4401860ecba3083584ea90365f6133e76a1fe10654f","observation_id":"09a9e9b7-3161-4019-a9bd-d42e701a9ed9","resolution":{"observed_at":"2026-08-10T18:43:48.815075Z","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":"2026-08-10T18:43:49.851815Z","title":"Communication-efficient fed- erated deep learning with layerwise asynchronous model update and tem- porally weighted aggregation","venue":null,"work_id":"e971dfe0-40db-4915-ae54-7abf4f508650","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.820416Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:6a757032efb94a237636e47dc34bbc425470786bed5df5f1aee83daee1d7d4a5","observation_id":"26d617b6-6a40-40e5-b5c1-b40e62ef775e","resolution":{"observed_at":"2026-08-10T18:43:49.856898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.836663Z","title":"Asyn- chronous online federated learning for edge devices with non-IID data","venue":null,"work_id":"243961ad-20fe-4dc3-be7a-9bae5bff41c6","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.825734Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:f2bbb388fea87a6f13536f851e189cf79fd0330d6b638489c1620cdc8bac04ff","observation_id":"ca856c90-4146-4936-974b-b5dce0718760","resolution":{"observed_at":"2026-08-10T18:43:49.841321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.821113Z","title":"Federated learning with multichannel ALOHA","venue":null,"work_id":"f2b7ea60-d5bf-416f-a582-7adfdfbf39eb","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.830972Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:aac49d961d9ee50b875e2e340ac9075a8783e40d887ec09a06bd621005d21766","observation_id":"4c36b0e1-fe7a-443c-8586-c019d06098f0","resolution":{"observed_at":"2026-08-10T18:43:49.826384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.835336Z","title":"Support-vector networks","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.835336Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:d309f912ede5b1a05ba7cadf57d0e85a006a49a103b6f8afba6467e8b16852d7","observation_id":"3707d78d-0c80-4004-865e-8b804d02e76d","resolution":{"observed_at":"2026-08-10T18:43:48.835336Z","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":"2026-08-10T18:43:49.794643Z","title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients","venue":null,"work_id":"c71fe61c-a34f-4a16-9302-d86d85eb2ade","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.839506Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:ab936841d8153b7776f4300178c2ae62240aebf6b1d6129eb681e4903cffca80","observation_id":"cd93f7b2-a4f5-4a5b-9745-5a20c2d518cc","resolution":{"observed_at":"2026-08-10T18:43:49.799835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.777834Z","title":"Rawat, and Chunmei Liu","venue":null,"work_id":"aed97284-296a-48e9-b8be-a09a5903eeb6","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.843673Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:01bd40e6e7f6f1ee805f11208f50947dd1e5b0c5c898a4a3678d0e6faf157cab","observation_id":"3c93261d-a35a-4fa9-96b5-0dc1bd4c8930","resolution":{"observed_at":"2026-08-10T18:43:49.782360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.762515Z","title":"https://data.europa.eu/eli/reg/2016/679/oj, 2016","venue":null,"work_id":"a0216b40-4298-469b-94ca-28d762ca21c7","year":2016},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.848297Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:fb8f913ad28fb521f9ac73eb1f7475e2ae28fd68b36b92918c47fbf84f0ef143","observation_id":"650cbccf-7266-4852-beaa-2d188799434e","resolution":{"observed_at":"2026-08-10T18:43:49.767687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.746620Z","title":"https: //en.wikipedia.org/wiki/Federated_learning#/media/File: Federated_learning_(centralized_vs_decentralized).png, 2023","venue":null,"work_id":"74195390-898a-4f4a-bbba-179f0322466b","year":2023},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.852864Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:38320dce693ba1237f06a6413673aff7ed14f6f33cd01329ed08969d7cff29e7","observation_id":"4bb5c136-58a2-4c5c-9468-71d982421583","resolution":{"observed_at":"2026-08-10T18:43:49.751915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.730566Z","title":"https://flower.ai/static/ images/blog/content/2023-11-29-xgboost-fl.jpg , 2023","venue":null,"work_id":"be9140da-7344-4983-a0f5-5f477e8618f4","year":2023},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.857863Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:8b0e6eebe492fe44261c9b171116a2049a65302ac6b7d59b95c9cb3e5a084db0","observation_id":"13599ebc-4778-4c6e-a870-dcf6d29d1732","resolution":{"observed_at":"2026-08-10T18:43:49.735831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.715487Z","title":"https://flower.ai/docs/framework/tutorial-quickstart- xgboost.html#tree-based-bagging-aggregation, 2023","venue":null,"work_id":"994b9025-a278-4feb-a4b5-64bb560ed7bd","year":2023},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.862314Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:1c83504293da1cce51603506e53b5dc1c0889259f3cf021cd3571525cebcdc68","observation_id":"6b2af9b3-9855-40d4-a594-260834bf78a5","resolution":{"observed_at":"2026-08-10T18:43:49.720282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.866648Z","title":"Long short-term memory.Neural Computation, 9(8):1735–1780, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.866648Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:8c6ad97fa82badf75991e96f201ef4ccb13667aeb8e4396ecffeab7ba8d8213d","observation_id":"01070cb7-aef0-4d5a-b6d4-f0489cc0a9fc","resolution":{"observed_at":"2026-08-10T18:43:48.866648Z","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":"2026-08-10T18:43:49.688386Z","title":"Model and feature aggre- gation based federated learning for multi-sensor time series trend following","venue":null,"work_id":"c42476dc-4562-4839-99a2-6157e6dc5145","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.871135Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:19a935f31fb36f24d4e43cf726fdcdcb16a55bc11c75c265194fdac815cef7a2","observation_id":"4e17d64d-5a1b-4da8-bf06-fae7a23c27b5","resolution":{"observed_at":"2026-08-10T18:43:49.692940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.673799Z","title":"Incentive design for efficient federated learning in mobile networks: A contract theory approach","venue":null,"work_id":"d2f65f94-c0b3-45c1-aaf9-ad90838c4575","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.875959Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:63dc47f82f9a759d2cb57cb2b6ba1c01124ada60e166c5b6e12e027420352033","observation_id":"9552157e-7793-4c16-a887-fd179f818246","resolution":{"observed_at":"2026-08-10T18:43:49.678583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.658295Z","title":"Reliable federated learning for mobile networks","venue":null,"work_id":"a3dc661a-4962-4e59-b88b-af375a6e9525","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.880311Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:7cb993ebf7804f2948b4efdd8fb66c00c2373f5f1d8b6cab0219b9a9e34e6510","observation_id":"c2c0420b-62e3-40f8-8b0a-5c1fd469184d","resolution":{"observed_at":"2026-08-10T18:43:49.663130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.642226Z","title":"Blockchained on-device federated learning","venue":null,"work_id":"f212e2be-db69-4d8d-995e-b4ea7c57ff9c","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.884734Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:6753e1c5d30bf6c04a26827e90183a021591a400c3f22ee59ecb0959df7ae407","observation_id":"076f5ac6-25be-4130-b2b4-552de1623b66","resolution":{"observed_at":"2026-08-10T18:43:49.647981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.625900Z","title":"Peer-to-peer federated learning on graphs","venue":null,"work_id":"cdbcd0cd-c68c-4158-a896-764eed8a8d02","year":1901},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.888831Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:1134244687ce7366a4b95076cae12c3c8a9305264975374ee8eedb68dcca08fe","observation_id":"a0642392-22d6-45cb-b84d-c5d46f25cd42","resolution":{"observed_at":"2026-08-10T18:43:49.631216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.893104Z","title":"MNIST handwritten digit database","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.893104Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:8082c04c35aa38564bfce80b10324a9002a9ebe1b3b0b3603f334a40b57367fb","observation_id":"76870c1b-440d-4f74-b22d-0bfc38e3b543","resolution":{"observed_at":"2026-08-10T18:43:48.893104Z","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":"2026-08-10T18:43:49.600452Z","title":"Backpropagation applied to handwritten zip code recog- nition","venue":null,"work_id":"33060b8b-6f55-480b-a52d-6ce8e89e3e56","year":1989},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.897751Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:0501ff575f4156280e54e3f268738e4eb95270fc47ab592652a9e4fa8350da94","observation_id":"6d5d1019-e2f2-4e69-b70f-3c8a5a7d49ae","resolution":{"observed_at":"2026-08-10T18:43:49.605175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10497","last_updated":"2020-02-14T22:48:28Z","snapshot_observed_at":"2026-08-14T16:26:48.571116Z","submitted_at":"2019-05-25T01:47:41Z","title":"Fair Resource Allocation in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10497","snapshot_observed_at":"2026-08-10T18:43:48.902114Z","title":"Fair resource allocation in federated learning","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.902114Z"},"links":{"cited_paper":"/paper/1905.10497","citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:21c6ff4fa3b3bab7ba6d197e601ef02a5c8dc8591a62b303c0c86ef4ce1ace96","observation_id":"ac14a11a-ee63-4a1c-9d26-e69d9d608cb0","resolution":{"observed_at":"2026-08-10T18:43:48.902114Z","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":"2026-08-10T18:43:49.585161Z","title":"Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption , pages 154–179","venue":null,"work_id":"7f9ba8d6-9559-4131-abe6-a125394899b8","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.907242Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:f0b8e1c833202d185f156b9a5fa688e2cd1f399afb6d27da9eb4eed479aab70c","observation_id":"4937f759-9383-46c3-8e3c-ca52ce94eceb","resolution":{"observed_at":"2026-08-10T18:43:49.590513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.570002Z","title":"Isolation forest","venue":null,"work_id":"44c38fb6-450e-493f-9779-b4a102041a10","year":2008},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.911443Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:0bab4c9bfb4869f719a94c6d50d9663a77f7ea1dedc068751c2f176e0a12bc2b","observation_id":"5f844349-2211-428c-ae59-609d658496e7","resolution":{"observed_at":"2026-08-10T18:43:49.575080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.555565Z","title":"H Deng, and Kui Ren","venue":null,"work_id":"720bba22-afaa-4fbc-8e46-1c9762fea9f3","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.916028Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:d23a3968caed44147a20a5b5fc69f2233c40d7afb530e4f741c5e401bafd1e9f","observation_id":"e6bcf8de-6caa-465d-8173-a9bf0984f79c","resolution":{"observed_at":"2026-08-10T18:43:49.559606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.538436Z","title":"Blockchain and federated learning for privacy-preserved data sharing in industrial iot","venue":null,"work_id":"b793bad0-973d-4dc2-9e35-c8e3f4648979","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.920117Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:e1cc640a1d3c50a6a466c9f6d9d4c4f5d9dc562c6af10522f502ee4fe276e61e","observation_id":"074d9b3a-add1-4433-b3db-d62dfe106e16","resolution":{"observed_at":"2026-08-10T18:43:49.543593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.521272Z","title":"FLchain: Federated learning via MEC-enabled blockchain network","venue":null,"work_id":"f08e7a5b-16a0-4112-956a-d766698a0759","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.924106Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:6cdb3d98749613a98c9121f3b12bb561b25eb583560e15a2f7b953fb6e2eb040","observation_id":"e64a69f8-ea6f-4d55-bedf-c84d8f93e84f","resolution":{"observed_at":"2026-08-10T18:43:49.526391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.505973Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":"7238443a-92dc-452a-8101-e6abf3d13ed0","year":2017},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.928455Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:ea46293aa92036ade746beecadb7e934486a9b92c420b133eff4d1487d8337be","observation_id":"2c26f0fc-413a-42a7-8629-611ae7fdd54c","resolution":{"observed_at":"2026-08-10T18:43:49.511029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.491031Z","title":"https://numpy.org/","venue":null,"work_id":"082acdb7-4eee-4b75-93cd-060cd04ad80e","year":null},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.932910Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:dfc367ea8f49df1071c43bba900af552359b4cc06aba6d23a60b939cc0131b11","observation_id":"c8b9021d-aae0-4e33-8d54-6f9e6ec31c3d","resolution":{"observed_at":"2026-08-10T18:43:49.495787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.475583Z","title":"https://pandas.pydata.org/","venue":null,"work_id":"f70fc765-f09d-467b-8b56-314eab0d85d9","year":null},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.937311Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:038c5ce445cf598a42926abc5a16297a2b6b2f297f855e88c4af1126cabd3efe","observation_id":"9404087a-f398-493b-a294-e3d67677b3e4","resolution":{"observed_at":"2026-08-10T18:43:49.480870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.941674Z","title":"https://pytorch.org/","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.941674Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:32bf823cbc6142573de18f34dfefd5f22c513f1529d5853ad7a1dbb81acfc696","observation_id":"93a12088-f2e9-4f40-9d24-f15661792bac","resolution":{"observed_at":"2026-08-10T18:43:48.941674Z","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":"2026-08-10T18:43:49.448452Z","title":"Fedpaq: A communication-efficient federated learn- ing method with periodic averaging and quantization","venue":null,"work_id":"3b25e73d-c840-4460-bffb-e18ad41a1d25","year":2021},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.946343Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:a18972e8f7397df0f72ae5231ac18d02846790977ff9474cd3de8765f9234caf","observation_id":"903d0a26-b4c7-499f-b958-ed1148ef8b0f","resolution":{"observed_at":"2026-08-10T18:43:49.453149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:48.951003Z","title":"A fast algorithm for the minimum covariance determinant estimator","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.951003Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:a8729e51c937217e57b7301a54240b137d75676fd691c3723b23d0872870826e","observation_id":"937349c6-55b3-4160-9efa-59e37711fb7a","resolution":{"observed_at":"2026-08-10T18:43:48.951003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.06731","last_updated":"2019-05-16T13:23:49Z","snapshot_observed_at":"2026-08-14T16:31:14.756530Z","submitted_at":"2019-05-16T13:23:49Z","title":"BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.06731","snapshot_observed_at":"2026-08-10T18:43:48.955766Z","title":"Braintorrent: A peer-to-peer environment for decen- tralized federated learning","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.955766Z"},"links":{"cited_paper":"/paper/1905.06731","citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:a8a2f4dec84608cad6da10701ad3bac0235098d4aee01cbf08647d9933cab126","observation_id":"371f51e6-cb02-4205-bc3a-0b93f728b1e8","resolution":{"observed_at":"2026-08-10T18:43:48.955766Z","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":"2026-08-10T18:43:49.422023Z","title":"Rumelhart, Geoffrey E","venue":null,"work_id":"ae82d639-d768-43f8-85ae-d1e2134a3bd8","year":1986},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.965201Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:6a96d3d8bd89b181bc99a516f650d5d4a093aa478b90912673a9f360a2a3d194","observation_id":"babcddcc-6206-4368-be67-aa762090ea44","resolution":{"observed_at":"2026-08-10T18:43:49.426700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.405877Z","title":"Samy and Sarunas Girdzijauskas","venue":null,"work_id":"003f1c9b-6367-465b-bbfe-188c4470328e","year":2023},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.970036Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:34fca4bcbef7e74ace9d0e35915babd047ca855b5cf38fbd2cdfe3700db62dc2","observation_id":"57017db1-bce0-4194-875a-18847d0f1489","resolution":{"observed_at":"2026-08-10T18:43:49.411486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.390987Z","title":"Platt, John Shawe-Taylor, Alex J","venue":null,"work_id":"a7741651-42d4-43bb-a246-2c94aa4d8ade","year":2001},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.975058Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:bfb8b15db56f017e350fcb22dcb9dc1108296405c9d5ad0203cee498496c8da3","observation_id":"631d8340-aa30-43a2-bf85-596f69718039","resolution":{"observed_at":"2026-08-10T18:43:49.395694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.374304Z","title":"https://scikit-learn.org/ stable/","venue":null,"work_id":"dffa4b23-5865-4093-b1d4-e8d778e4bb03","year":null},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.979716Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:24c01c392b74c8807e58bada739292d0218906bb4d70b8e8629ee6285237652a","observation_id":"e68b48d1-6f5f-494b-9090-db19725836e1","resolution":{"observed_at":"2026-08-10T18:43:49.379461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.00792","last_updated":"2018-11-10T15:37:17Z","snapshot_observed_at":"2026-08-14T19:29:54.526857Z","submitted_at":"2018-04-03T02:24:31Z","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.00792","snapshot_observed_at":"2026-08-10T18:43:48.984357Z","title":"Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.984357Z"},"links":{"cited_paper":"/paper/1804.00792","citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:fded5bbf36557d454e5fd4700d8027ac0239bb3fe1c0f9f7f300486c4453f1bd","observation_id":"1d93f90f-2786-44cb-a11f-ae15b8cd9293","resolution":{"observed_at":"2026-08-10T18:43:48.984357Z","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":"2026-08-10T18:43:49.358562Z","title":"Measure contribution of participants in federated learning","venue":null,"work_id":"b14af0f9-a437-4dd0-a312-6da9e82cf1e6","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.989449Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:420c30156326e509e8ee3236fe52ae9fe7344e0fae40e8206f13b0a7beacec54","observation_id":"125f19d2-47c1-4017-ab5a-ec54437bd147","resolution":{"observed_at":"2026-08-10T18:43:49.362953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.343361Z","title":"https://xgboost","venue":null,"work_id":"6c235322-65ca-4b02-8aec-bca84224a4b8","year":null},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.994129Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:46dc175ab17892d16336f09b2c0390bc62de69b8282a5c0b60644fa1f561ea9f","observation_id":"66822dab-129f-4da4-b321-0ff0e0fbeaef","resolution":{"observed_at":"2026-08-10T18:43:49.348124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.325773Z","title":"Hybridalpha: An efficient approach for privacy-preserving federated learn- ing","venue":null,"work_id":"1821b28c-f9bf-4c39-9ddb-bd48746aceb1","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:48.998369Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:7a8be5112b8573610f0895304f1730c675f8c8014bb33d250e31fdf38a974bb9","observation_id":"38c7438d-7485-4dbd-a4d5-ad3b56d2882a","resolution":{"observed_at":"2026-08-10T18:43:49.330994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.309665Z","title":"Fed- erated learning","venue":null,"work_id":"d1c3ffc7-66b0-4e41-9572-242cd60c5ddb","year":2019},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:49.002635Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:47ec1dada3b081847d132cc1b724f3d3fbb59f3c191c099187ca466e4ec5e830","observation_id":"262784b8-f442-4d3a-9b96-8ebb25c259ec","resolution":{"observed_at":"2026-08-10T18:43:49.314343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T18:43:49.293175Z","title":"Experience-driven computational resource allocation of federated learning by deep reinforcement learning","venue":null,"work_id":"0affc02d-7e88-4af4-b970-70c8019e9864","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:49.006919Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:8c3c82b2d48bf7d404009f5b3782696687b2b219113b038266e2766f2c6c617d","observation_id":"89c26ec8-de5e-4621-b369-65ac187a22fb","resolution":{"observed_at":"2026-08-10T18:43:49.298730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7250.89960","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:43:49.181335Z","title":"Liu, and Yang Xiang","venue":null,"work_id":"7bee2180-2cf4-4f7e-9b5a-44d555eb468b","year":2020},"citing_paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:49.010684Z"},"links":{"citing_paper":"/paper/2501.11054"},"observation_digest":"sha256:559c05c0419f04804f0f6fce2d50cd2422d366c8a92ade30f0dbf25c71201f8c","observation_id":"9bc16758-82af-49c1-bbc5-95c77bfdfa23","resolution":{"observed_at":"2026-08-10T18:43:49.192407Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.11054","last_updated":"2025-01-19T14:09:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T18:38:41.735890Z","submitted_at":"2025-01-19T14:09:13Z","title":"Temporal Analysis of Adversarial Attacks in Federated Learning"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":40},"total_outbound_references":52},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.11054."}