{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WGBYCLJL6CJ5J5MSEJ6S2JT4FC","short_pith_number":"pith:WGBYCLJL","schema_version":"1.0","canonical_sha256":"b183812d2bf093d4f592227d2d267c289b6de64cdda29d8b8f7e86256d213fe3","source":{"kind":"arxiv","id":"2408.08214","version":1},"attestation_state":"computed","paper":{"title":"Federated Fairness Analytics: Quantifying Fairness in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.GT","cs.NE"],"primary_cat":"cs.LG","authors_text":"Dimitra Simeonidou, Juan Marcelo Parra-Ullauri, Oscar Dilley, Rasheed Hussain","submitted_at":"2024-08-15T15:23:32Z","abstract_excerpt":"Federated Learning (FL) is a privacy-enhancing technology for distributed ML. By training models locally and aggregating updates - a federation learns together, while bypassing centralised data collection. FL is increasingly popular in healthcare, finance and personal computing. However, it inherits fairness challenges from classical ML and introduces new ones, resulting from differences in data quality, client participation, communication constraints, aggregation methods and underlying hardware. Fairness remains an unresolved issue in FL and the community has identified an absence of succinct"},"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":"2408.08214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-15T15:23:32Z","cross_cats_sorted":["cs.AI","cs.DC","cs.GT","cs.NE"],"title_canon_sha256":"835bdac95864d09efa39f7bf7d1f46ae78e5dbcc7c63ec636c695966c203190d","abstract_canon_sha256":"914948af7f9eca9ec09e1a3223d04d635ddc37190bf37757c160608cefd9ab09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:55:45.950174Z","signature_b64":"1+0vGjUkMp9SiQgbidiXen4xdE+9shlGsBzxx5nAFVDXh2VUT6sYLGYTpKa1nKNdXdugQtAiNtLgJCbsQoaOCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b183812d2bf093d4f592227d2d267c289b6de64cdda29d8b8f7e86256d213fe3","last_reissued_at":"2026-07-05T08:55:45.949643Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:55:45.949643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Fairness Analytics: Quantifying Fairness in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.GT","cs.NE"],"primary_cat":"cs.LG","authors_text":"Dimitra Simeonidou, Juan Marcelo Parra-Ullauri, Oscar Dilley, Rasheed Hussain","submitted_at":"2024-08-15T15:23:32Z","abstract_excerpt":"Federated Learning (FL) is a privacy-enhancing technology for distributed ML. By training models locally and aggregating updates - a federation learns together, while bypassing centralised data collection. FL is increasingly popular in healthcare, finance and personal computing. However, it inherits fairness challenges from classical ML and introduces new ones, resulting from differences in data quality, client participation, communication constraints, aggregation methods and underlying hardware. Fairness remains an unresolved issue in FL and the community has identified an absence of succinct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08214","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/2408.08214/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":"2408.08214","created_at":"2026-07-05T08:55:45.949701+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.08214v1","created_at":"2026-07-05T08:55:45.949701+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08214","created_at":"2026-07-05T08:55:45.949701+00:00"},{"alias_kind":"pith_short_12","alias_value":"WGBYCLJL6CJ5","created_at":"2026-07-05T08:55:45.949701+00:00"},{"alias_kind":"pith_short_16","alias_value":"WGBYCLJL6CJ5J5MS","created_at":"2026-07-05T08:55:45.949701+00:00"},{"alias_kind":"pith_short_8","alias_value":"WGBYCLJL","created_at":"2026-07-05T08:55:45.949701+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/WGBYCLJL6CJ5J5MSEJ6S2JT4FC","json":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC.json","graph_json":"https://pith.science/api/pith-number/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/graph.json","events_json":"https://pith.science/api/pith-number/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/events.json","paper":"https://pith.science/paper/WGBYCLJL"},"agent_actions":{"view_html":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC","download_json":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC.json","view_paper":"https://pith.science/paper/WGBYCLJL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.08214&json=true","fetch_graph":"https://pith.science/api/pith-number/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/graph.json","fetch_events":"https://pith.science/api/pith-number/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/action/storage_attestation","attest_author":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/action/author_attestation","sign_citation":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/action/citation_signature","submit_replication":"https://pith.science/pith/WGBYCLJL6CJ5J5MSEJ6S2JT4FC/action/replication_record"}},"created_at":"2026-07-05T08:55:45.949701+00:00","updated_at":"2026-07-05T08:55:45.949701+00:00"}