{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DWNMIVEVXOIDW3VOGECLHFJKHU","short_pith_number":"pith:DWNMIVEV","schema_version":"1.0","canonical_sha256":"1d9ac45495bb903b6eae3104b3952a3d30bb83e2f54d61e783f9547944aeeb57","source":{"kind":"arxiv","id":"2303.11673","version":1},"attestation_state":"computed","paper":{"title":"A Survey on Class Imbalance in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chuanwen Li, Jianzgong Qi, Jiayuan He, Jing Zhang","submitted_at":"2023-03-21T08:34:23Z","abstract_excerpt":"Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode, especially when the training data is imbalanced. In the context of federated learning, data imbalance may occur either locally one one client device, or globally across many device"},"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":"2303.11673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T08:34:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b4e169c4262ac2486e89da5084df95350ee73cae4c972606c4bed9f573376501","abstract_canon_sha256":"02446d76378f6c395a6b7fa697d32a7f6f34a3adceabffe2478eaa323cea7fa8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:53:20.888470Z","signature_b64":"P5GpmKUthLt3JGVBhlBLRmnzUxS6+Tg9jQg9kiNueiq+0EddN+2OrnBTRzVngUV67QCPzALwA+HpjWDr+ndbCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d9ac45495bb903b6eae3104b3952a3d30bb83e2f54d61e783f9547944aeeb57","last_reissued_at":"2026-07-05T05:53:20.888059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:53:20.888059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Class Imbalance in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chuanwen Li, Jianzgong Qi, Jiayuan He, Jing Zhang","submitted_at":"2023-03-21T08:34:23Z","abstract_excerpt":"Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode, especially when the training data is imbalanced. In the context of federated learning, data imbalance may occur either locally one one client device, or globally across many device"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.11673","kind":"arxiv","version":1},"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/2303.11673/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":"2303.11673","created_at":"2026-07-05T05:53:20.888117+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.11673v1","created_at":"2026-07-05T05:53:20.888117+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.11673","created_at":"2026-07-05T05:53:20.888117+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWNMIVEVXOID","created_at":"2026-07-05T05:53:20.888117+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWNMIVEVXOIDW3VO","created_at":"2026-07-05T05:53:20.888117+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWNMIVEV","created_at":"2026-07-05T05:53:20.888117+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01474","citing_title":"Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU","json":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU.json","graph_json":"https://pith.science/api/pith-number/DWNMIVEVXOIDW3VOGECLHFJKHU/graph.json","events_json":"https://pith.science/api/pith-number/DWNMIVEVXOIDW3VOGECLHFJKHU/events.json","paper":"https://pith.science/paper/DWNMIVEV"},"agent_actions":{"view_html":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU","download_json":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU.json","view_paper":"https://pith.science/paper/DWNMIVEV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.11673&json=true","fetch_graph":"https://pith.science/api/pith-number/DWNMIVEVXOIDW3VOGECLHFJKHU/graph.json","fetch_events":"https://pith.science/api/pith-number/DWNMIVEVXOIDW3VOGECLHFJKHU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU/action/storage_attestation","attest_author":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU/action/author_attestation","sign_citation":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU/action/citation_signature","submit_replication":"https://pith.science/pith/DWNMIVEVXOIDW3VOGECLHFJKHU/action/replication_record"}},"created_at":"2026-07-05T05:53:20.888117+00:00","updated_at":"2026-07-05T05:53:20.888117+00:00"}