{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6WAXBAFHZ2Y6QLU7FW3O26VCH3","short_pith_number":"pith:6WAXBAFH","schema_version":"1.0","canonical_sha256":"f5817080a7ceb1e82e9f2db6ed7aa23ef4e4ff78ccc0971d990f35919831d218","source":{"kind":"arxiv","id":"2109.08344","version":3},"attestation_state":"computed","paper":{"title":"Achieving Model Fairness in Vertical Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Changxin Liu, Jian Pei, Lingyang Chu, Yang Shi, Yong Zhang, Zhenan Fan, Zirui Zhou","submitted_at":"2021-09-17T04:40:11Z","abstract_excerpt":"Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learning models without disclosing their private data and model parameters. Similar to other machine learning algorithms, VFL faces demands and challenges of fairness, i.e., the learned model may be unfairly discriminatory over some groups with sensitive attributes. To tackle this problem, we propose a fair VFL framework in this work. First, we systematically formulate the problem of training fair models in VFL, where the"},"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":"2109.08344","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-17T04:40:11Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"7e7e1db081ac6e7cb13b5446bc73668a304fec8d3def4e462a650992d52abaa4","abstract_canon_sha256":"1d8635929c7c2c9cff9521fb6846879330a1677d4b6d608de0cd116327ab5f6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:54:46.788151Z","signature_b64":"hk6Kf7E+IaRQLuX1u3zOkvmAlH4/mZNve66IBV3OsJ0XYvSA1lx6AAiVAlSCZ0n10NKjNbcYzHjRVo1qnTb4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5817080a7ceb1e82e9f2db6ed7aa23ef4e4ff78ccc0971d990f35919831d218","last_reissued_at":"2026-07-05T04:54:46.787700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:54:46.787700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Achieving Model Fairness in Vertical Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Changxin Liu, Jian Pei, Lingyang Chu, Yang Shi, Yong Zhang, Zhenan Fan, Zirui Zhou","submitted_at":"2021-09-17T04:40:11Z","abstract_excerpt":"Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learning models without disclosing their private data and model parameters. Similar to other machine learning algorithms, VFL faces demands and challenges of fairness, i.e., the learned model may be unfairly discriminatory over some groups with sensitive attributes. To tackle this problem, we propose a fair VFL framework in this work. First, we systematically formulate the problem of training fair models in VFL, where the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08344","kind":"arxiv","version":3},"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/2109.08344/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":"2109.08344","created_at":"2026-07-05T04:54:46.787758+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.08344v3","created_at":"2026-07-05T04:54:46.787758+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08344","created_at":"2026-07-05T04:54:46.787758+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WAXBAFHZ2Y6","created_at":"2026-07-05T04:54:46.787758+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WAXBAFHZ2Y6QLU7","created_at":"2026-07-05T04:54:46.787758+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WAXBAFH","created_at":"2026-07-05T04:54:46.787758+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.01851","citing_title":"Mitigating Group-Level Fairness Disparities in Federated Visual Language Models","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3","json":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3.json","graph_json":"https://pith.science/api/pith-number/6WAXBAFHZ2Y6QLU7FW3O26VCH3/graph.json","events_json":"https://pith.science/api/pith-number/6WAXBAFHZ2Y6QLU7FW3O26VCH3/events.json","paper":"https://pith.science/paper/6WAXBAFH"},"agent_actions":{"view_html":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3","download_json":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3.json","view_paper":"https://pith.science/paper/6WAXBAFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.08344&json=true","fetch_graph":"https://pith.science/api/pith-number/6WAXBAFHZ2Y6QLU7FW3O26VCH3/graph.json","fetch_events":"https://pith.science/api/pith-number/6WAXBAFHZ2Y6QLU7FW3O26VCH3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3/action/storage_attestation","attest_author":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3/action/author_attestation","sign_citation":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3/action/citation_signature","submit_replication":"https://pith.science/pith/6WAXBAFHZ2Y6QLU7FW3O26VCH3/action/replication_record"}},"created_at":"2026-07-05T04:54:46.787758+00:00","updated_at":"2026-07-05T04:54:46.787758+00:00"}