{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZLGTZUNCLNRWJ62NCZQFGOIA4G","short_pith_number":"pith:ZLGTZUNC","schema_version":"1.0","canonical_sha256":"cacd3cd1a25b6364fb4d1660533900e185f8a063ec006b872673592c9b7f88a0","source":{"kind":"arxiv","id":"2410.11397","version":2},"attestation_state":"computed","paper":{"title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaochao Chen, Fengyuan Yu, Jiahe Xu, Jun Wang, Pengyang Zhou, Weiming Liu, Wenjie Wang, Xiaolin Zheng, Xinting Liao","submitted_at":"2024-10-15T08:39:31Z","abstract_excerpt":"Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data and unexpected out-of-distribution (OOD) data, such as covariate-shift and semantic-shift data. Current FL researches typically address either covariate-shift data through OOD generalization or semantic-shift data via OOD detection, overlooking the simultaneous occurrence of various OOD shifts. In this work, we propose FOOGD, a method that estimate"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.11397","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-15T08:39:31Z","cross_cats_sorted":[],"title_canon_sha256":"c85df28a3e01196780a36d583f60482ddd3a2d138e808bfc4e72a3ab529b7467","abstract_canon_sha256":"e82c814648db98c644c483005f320a20cd4fd74fa520f9ca8e009e3408d6ef55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:39.414354Z","signature_b64":"FTnsWaSbAlvpei6eCg55TIxLBCHcO/T/Yt2SdPBMMT8TcjxycPxlAgUJRykRW0VrJuz6LmWgpgFDXa5bZ4ZbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cacd3cd1a25b6364fb4d1660533900e185f8a063ec006b872673592c9b7f88a0","last_reissued_at":"2026-07-05T09:24:39.413834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:39.413834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaochao Chen, Fengyuan Yu, Jiahe Xu, Jun Wang, Pengyang Zhou, Weiming Liu, Wenjie Wang, Xiaolin Zheng, Xinting Liao","submitted_at":"2024-10-15T08:39:31Z","abstract_excerpt":"Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data and unexpected out-of-distribution (OOD) data, such as covariate-shift and semantic-shift data. Current FL researches typically address either covariate-shift data through OOD generalization or semantic-shift data via OOD detection, overlooking the simultaneous occurrence of various OOD shifts. In this work, we propose FOOGD, a method that estimate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11397","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.11397/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.11397","created_at":"2026-07-05T09:24:39.413899+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11397v2","created_at":"2026-07-05T09:24:39.413899+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11397","created_at":"2026-07-05T09:24:39.413899+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLGTZUNCLNRW","created_at":"2026-07-05T09:24:39.413899+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLGTZUNCLNRWJ62N","created_at":"2026-07-05T09:24:39.413899+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLGTZUNC","created_at":"2026-07-05T09:24:39.413899+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.16218","citing_title":"FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G","json":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G.json","graph_json":"https://pith.science/api/pith-number/ZLGTZUNCLNRWJ62NCZQFGOIA4G/graph.json","events_json":"https://pith.science/api/pith-number/ZLGTZUNCLNRWJ62NCZQFGOIA4G/events.json","paper":"https://pith.science/paper/ZLGTZUNC"},"agent_actions":{"view_html":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G","download_json":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G.json","view_paper":"https://pith.science/paper/ZLGTZUNC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11397&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLGTZUNCLNRWJ62NCZQFGOIA4G/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLGTZUNCLNRWJ62NCZQFGOIA4G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G/action/storage_attestation","attest_author":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G/action/author_attestation","sign_citation":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G/action/citation_signature","submit_replication":"https://pith.science/pith/ZLGTZUNCLNRWJ62NCZQFGOIA4G/action/replication_record"}},"created_at":"2026-07-05T09:24:39.413899+00:00","updated_at":"2026-07-05T09:24:39.413899+00:00"}