{"as_of":"2026-08-22T22:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dfe63962dccb8398f59245058266729c6afad53e4d5f3001d5e5ba9e7c939031","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:49:01.072591Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2608.04073/citation-record","integrity":"/paper/2608.04073/integrity","json":"/paper/2608.04073/citation-record.json","paper":"/paper/2608.04073"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:49:00.913536Z","title":"Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.913536Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:e283cffd3e61d25a8df0b923fe1ad66c8e2b3d09f6c2e55ab1b04e18651675ec","observation_id":"5fd5409d-cea1-446e-ba39-d6f4a81861e2","resolution":{"observed_at":"2026-08-15T14:49:00.913536Z","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":"2026-08-15T14:49:01.612020Z","title":"Surveying trust-based collaborative intrusion detection: State-of-the-art, challenges and future directions,","venue":null,"work_id":"c0c6918b-18d1-4239-bf56-6e70967fc3e3","year":2022},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.924434Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:ec9777b3385655faad99225d11e9cfa7aa7db384b4d3034b452bbb81996e05e4","observation_id":"5af4e3ca-6fbb-4133-8113-3a3e89e512b4","resolution":{"observed_at":"2026-08-15T14:49:01.621029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.584106Z","title":"A survey on IoT intrusion detection: Federated learning, game theory, social psychology, and explainable AI as future directions,","venue":null,"work_id":"d21111d7-fbfc-4a7c-b250-45e57dcae80d","year":2022},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.936752Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:813efca203aa547b19c030e99ace56658963eec1848393695d7a8db2a633d6c9","observation_id":"be5e711a-52c9-4581-bd5a-2f4ae0fc0063","resolution":{"observed_at":"2026-08-15T14:49:01.591836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.545361Z","title":"Federated learning for internet of things: A compre- hensive survey,","venue":null,"work_id":"8a887ab5-d4fc-460c-919d-34fe5f7df546","year":2021},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.946763Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:e90e80c15fac13578ba0f72dffe0901b12a318fadfa9f488070dba8a01693ea1","observation_id":"64f3e312-6219-4c26-a732-a0847e6f32eb","resolution":{"observed_at":"2026-08-15T14:49:01.561763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.518824Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"cf4b0f6b-94e5-4745-b575-529f68cea6d1","year":2017},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.955165Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:c644fe517781f7337c469bc8faf6afc76d430ee9921df0f4cd02844008f3e33b","observation_id":"f9c67424-7a1e-4f6c-b4cd-51c4ecbdba3a","resolution":{"observed_at":"2026-08-15T14:49:01.527343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.494322Z","title":"Personalized federated learning for intelligent IoT applications: A cloud-edge based framework,","venue":null,"work_id":"d297c938-70fd-43e9-bd02-c40b9fcce391","year":2020},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.964877Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:d2a17470356276a3f132beea5d4814c42433725f55ec7f994e062b6ebe50b61f","observation_id":"009ffe63-59ec-4eca-a0b3-50bc7f9493bc","resolution":{"observed_at":"2026-08-15T14:49:01.501865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.464127Z","title":"Ditto: fair and robust federated learning through personal- ization,","venue":null,"work_id":"f0a6e5f9-064f-4571-ba70-cca7c50a668a","year":2021},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.973948Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:ab12675121326d47448f45db41137dba44fa936568e8c0beaa1b23b7467d31ad","observation_id":"f654f8eb-4c32-44cb-8e45-1f06d6b18dff","resolution":{"observed_at":"2026-08-15T14:49:01.472848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.13461","last_updated":"2020-11-06T04:07:31Z","snapshot_observed_at":"2026-08-18T19:15:55.760651Z","submitted_at":"2020-03-30T13:19:37Z","title":"Adaptive Personalized Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.13461","snapshot_observed_at":"2026-08-15T14:49:00.982541Z","title":"Adaptive personalized federated learning,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.982541Z"},"links":{"cited_paper":"/paper/2003.13461","citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:6301e4d72fac02cf46d0916063cbd4d379e9cb5a730528ab161e6c7095831616","observation_id":"de672f71-e8f5-4aec-a9f1-b4cefa76a918","resolution":{"observed_at":"2026-08-15T14:49:00.982541Z","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":"2026-08-15T14:49:01.431060Z","title":"FedALA: adaptive local aggregation for personalized federated learn- ing,","venue":null,"work_id":"47544896-2490-45ab-a84e-d1a9c1580cdf","year":2023},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.990629Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:f7b402745ddc7b55e379d940731aa0735980141338ac93d85e73eeced18dd05a","observation_id":"2d703cac-5028-4432-bbd7-708e57267f56","resolution":{"observed_at":"2026-08-15T14:49:01.438597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.400188Z","title":"Lower bounds and optimal algorithms for personalized federated learning,","venue":null,"work_id":"c95e4801-938e-44bc-9c51-151ace49039e","year":2020},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:00.998093Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:cc37d8b93f148436957f551e5c865a9ef7f80d74b6751951788477595e9dcd4a","observation_id":"c52de141-c8a2-43ba-b35c-7b8a2935ef89","resolution":{"observed_at":"2026-08-15T14:49:01.409250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.006697Z","title":"Adapt to adaptation: Learning personalization for cross-silo federated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.006697Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:f24c720b0f6000a39c76078b991db221ab815a604b871f4f8eecb12e554b632b","observation_id":"fd21d7d3-c4a2-42a7-a3a9-06f06bc5fbcb","resolution":{"observed_at":"2026-08-15T14:49:01.006697Z","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":"2026-08-15T14:49:01.373288Z","title":"A contextual-bandit approach to personalized news article recommendation,","venue":null,"work_id":"1d076681-885b-41e7-a218-4af0c82ac0b6","year":2010},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.016498Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:9591534a0c113b5a974eadaf47a7d9a9760e0dcb5ee4704ee62c9e21dc10fd31","observation_id":"9c5b0f89-bb43-469b-b8a7-3e2e75479cd3","resolution":{"observed_at":"2026-08-15T14:49:01.383376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.343450Z","title":"FedEff: efficient federated learning with optimal local epochs for heterogeneous clients,","venue":null,"work_id":"a2a82c4c-2e18-4dea-b0ad-b6c28153843a","year":2025},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.026010Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:a1772481096a82331fe24f17576c18172b08c93d618496ce73b0e3726d89d587","observation_id":"271c7e1b-1da4-413a-9e98-55ad5057a87f","resolution":{"observed_at":"2026-08-15T14:49:01.354863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.296568Z","title":"FedDdrl: Federated double deep reinforcement learning for heterogeneous IoT with adaptive early client termination and local epoch adjustment,","venue":null,"work_id":"0b10a4a8-5d95-4d84-bbd7-e0ee2bfd3ecd","year":2023},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.033921Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:f706eeb1334140df9bf9043416da739b9a2ee850f172e931b92351aba9a27608","observation_id":"fe78ec84-bebf-457f-8dee-3b3417b1704e","resolution":{"observed_at":"2026-08-15T14:49:01.305090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.267610Z","title":"CICIoT2023: a real-time dataset and benchmark for large- scale attacks in IoT environment,","venue":null,"work_id":"a6be6967-1dec-48d2-adf7-f28b765d9170","year":2023},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.043874Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:9c18345a90e09704cf39172551b3ccd3859d2cfea72a77e9e43eb31061e74165","observation_id":"a413f698-5418-44c9-83f9-4415c681c285","resolution":{"observed_at":"2026-08-15T14:49:01.276292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.241454Z","title":"PFLlib: a beginner-friendly and comprehensive personalized federated learning library and benchmark,","venue":null,"work_id":"b0df570f-6d62-4d0a-bf89-13bcc1d02e00","year":2025},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.059626Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:3699e7f07774c89a52e3c0a1a7c34197d5fc06f89c9aaabfe012e1e2d3d2d678","observation_id":"5ded3d1c-8db9-432c-a839-6c2296f8fe5c","resolution":{"observed_at":"2026-08-15T14:49:01.249973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T14:49:01.207171Z","title":"Multivariate stochastic approximation using a simultane- ous perturbation gradient approximation,","venue":null,"work_id":"a0b1689c-8ef8-40be-b870-de838468cc5b","year":1992},"citing_paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T14:49:01.072591Z"},"links":{"citing_paper":"/paper/2608.04073"},"observation_digest":"sha256:78e423407cd77c23b4c495bfb547cc05396920a49b712bc93feb3513823e31ba","observation_id":"5d25a44e-45cb-4d60-9904-783219f8089b","resolution":{"observed_at":"2026-08-15T14:49:01.221391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.04073","last_updated":"2026-08-04T15:59:33Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-18T06:20:00.622650Z","submitted_at":"2026-08-04T15:59:33Z","title":"FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":17},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2608.04073."}