{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L6VPSHZC7FG7J2HHLDZ67F4UF4","short_pith_number":"pith:L6VPSHZC","schema_version":"1.0","canonical_sha256":"5faaf91f22f94df4e8e758f3ef97942f14bf9811e6863e1f0ef2a462a11a6b4c","source":{"kind":"arxiv","id":"2506.20431","version":1},"attestation_state":"computed","paper":{"title":"Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Xing Ma","submitted_at":"2025-06-25T13:42:30Z","abstract_excerpt":"Federated learning aims to train a global model in a distributed environment that is close to the performance of centralized training. However, issues such as client label skew, data quantity skew, and other heterogeneity problems severely degrade the model's performance. Most existing methods overlook the scenario where only a small portion of clients participate in training within a large-scale client setting, whereas our experiments show that this scenario presents a more challenging federated learning task. Therefore, we propose a Knowledge Distillation with teacher-student Inequitable Agg"},"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":"2506.20431","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T13:42:30Z","cross_cats_sorted":[],"title_canon_sha256":"f76d76ac34424097ecc5414b731f2dcb738d61618eee4505c04ee5bf1a9b843d","abstract_canon_sha256":"1ca5662b505bd36f0405c3d18ee88b1e3d355522021f8a1f9b9bd43235a1cd23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:05.355068Z","signature_b64":"gaAISuTwB2G5dX3OdUqTXU2X/cumXVUwbX8Q06v4zz2aEaPDcysGxe86EbNk3M0uY0KUhXUVrSVMo9SBVpOgBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5faaf91f22f94df4e8e758f3ef97942f14bf9811e6863e1f0ef2a462a11a6b4c","last_reissued_at":"2026-07-05T11:27:05.354541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:05.354541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Xing Ma","submitted_at":"2025-06-25T13:42:30Z","abstract_excerpt":"Federated learning aims to train a global model in a distributed environment that is close to the performance of centralized training. However, issues such as client label skew, data quantity skew, and other heterogeneity problems severely degrade the model's performance. Most existing methods overlook the scenario where only a small portion of clients participate in training within a large-scale client setting, whereas our experiments show that this scenario presents a more challenging federated learning task. Therefore, we propose a Knowledge Distillation with teacher-student Inequitable Agg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20431","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/2506.20431/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":"2506.20431","created_at":"2026-07-05T11:27:05.354609+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20431v1","created_at":"2026-07-05T11:27:05.354609+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20431","created_at":"2026-07-05T11:27:05.354609+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6VPSHZC7FG7","created_at":"2026-07-05T11:27:05.354609+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6VPSHZC7FG7J2HH","created_at":"2026-07-05T11:27:05.354609+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6VPSHZC","created_at":"2026-07-05T11:27:05.354609+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4","json":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4.json","graph_json":"https://pith.science/api/pith-number/L6VPSHZC7FG7J2HHLDZ67F4UF4/graph.json","events_json":"https://pith.science/api/pith-number/L6VPSHZC7FG7J2HHLDZ67F4UF4/events.json","paper":"https://pith.science/paper/L6VPSHZC"},"agent_actions":{"view_html":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4","download_json":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4.json","view_paper":"https://pith.science/paper/L6VPSHZC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20431&json=true","fetch_graph":"https://pith.science/api/pith-number/L6VPSHZC7FG7J2HHLDZ67F4UF4/graph.json","fetch_events":"https://pith.science/api/pith-number/L6VPSHZC7FG7J2HHLDZ67F4UF4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4/action/storage_attestation","attest_author":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4/action/author_attestation","sign_citation":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4/action/citation_signature","submit_replication":"https://pith.science/pith/L6VPSHZC7FG7J2HHLDZ67F4UF4/action/replication_record"}},"created_at":"2026-07-05T11:27:05.354609+00:00","updated_at":"2026-07-05T11:27:05.354609+00:00"}