{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:IXB6OLK5JFETD7HBRHSHOY6U6Q","short_pith_number":"pith:IXB6OLK5","canonical_record":{"source":{"id":"2012.02447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-04T08:04:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"01fa11b6fc718b051f913bfe32e9dd6bc7c63901f5f831ab73db02700b14aeb5","abstract_canon_sha256":"044c87a4631191ff51fb1b8f14d9c0905a64415e1473563b7ecea68f0ab39a7d"},"schema_version":"1.0"},"canonical_sha256":"45c3e72d5d494931fce189e47763d4f41db2421677d27b4261f9c27d283a7470","source":{"kind":"arxiv","id":"2012.02447","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.02447","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"arxiv_version","alias_value":"2012.02447v1","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.02447","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_12","alias_value":"IXB6OLK5JFET","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_16","alias_value":"IXB6OLK5JFETD7HB","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_8","alias_value":"IXB6OLK5","created_at":"2026-07-05T01:56:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:IXB6OLK5JFETD7HBRHSHOY6U6Q","target":"record","payload":{"canonical_record":{"source":{"id":"2012.02447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-04T08:04:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"01fa11b6fc718b051f913bfe32e9dd6bc7c63901f5f831ab73db02700b14aeb5","abstract_canon_sha256":"044c87a4631191ff51fb1b8f14d9c0905a64415e1473563b7ecea68f0ab39a7d"},"schema_version":"1.0"},"canonical_sha256":"45c3e72d5d494931fce189e47763d4f41db2421677d27b4261f9c27d283a7470","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:56:59.680434Z","signature_b64":"tYxuRc69DfKnHXKjAWSPogcWY/gwx7oblSnoN4gN2XGVZ8UN/JdC7/HeX0JkjcU2p7uVXxZqZ2cZEAML6K1YBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45c3e72d5d494931fce189e47763d4f41db2421677d27b4261f9c27d283a7470","last_reissued_at":"2026-07-05T01:56:59.679970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:56:59.679970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.02447","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:56:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DIZ/t5Ag0R4zzNt4RqgfwldyiTDVNl3YOWvNVPZCFELxL7e7Ouzldvcim7MV1nmAOa22FHfNLUwV5U3mWZhyBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:16:12.513381Z"},"content_sha256":"f57c482220b3eb5c2d5c239c5e241f6a0ae899ef7c3c79b1e759d7af21d84567","schema_version":"1.0","event_id":"sha256:f57c482220b3eb5c2d5c239c5e241f6a0ae899ef7c3c79b1e759d7af21d84567"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:IXB6OLK5JFETD7HBRHSHOY6U6Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mitigating Bias in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Annie Abay, Ebube Chuba, Heiko Ludwig, Nathalie Baracaldo, Shashank Rajamoni, Yi Zhou","submitted_at":"2020-12-04T08:04:12Z","abstract_excerpt":"As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in which an aggregator orchestrates multiple parties to train a global model without sharing their training data. In this paper, we discuss causes of bias in FL and propose three pre-processing and in-processing methods to mitigate bias, without compromising data privacy, a key FL requirement. As data heterogeneity among parties is one of the challenging characteristics of FL, we conduct experiments over several data d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.02447","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/2012.02447/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:56:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VKGZQ/857fuQpQKrFGXET9VUGRoU46NLaP8uzk70xVu35ZRgla1xogD+S8p6uWYz5qK4ILl2o+NCjuTXM308DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:16:12.513911Z"},"content_sha256":"d07136772380a3409709ada178fa5a62e562889e86e526cb3084da85a1e4a9db","schema_version":"1.0","event_id":"sha256:d07136772380a3409709ada178fa5a62e562889e86e526cb3084da85a1e4a9db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/bundle.json","state_url":"https://pith.science/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T01:16:12Z","links":{"resolver":"https://pith.science/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q","bundle":"https://pith.science/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/bundle.json","state":"https://pith.science/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IXB6OLK5JFETD7HBRHSHOY6U6Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:IXB6OLK5JFETD7HBRHSHOY6U6Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"044c87a4631191ff51fb1b8f14d9c0905a64415e1473563b7ecea68f0ab39a7d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-04T08:04:12Z","title_canon_sha256":"01fa11b6fc718b051f913bfe32e9dd6bc7c63901f5f831ab73db02700b14aeb5"},"schema_version":"1.0","source":{"id":"2012.02447","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.02447","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"arxiv_version","alias_value":"2012.02447v1","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.02447","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_12","alias_value":"IXB6OLK5JFET","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_16","alias_value":"IXB6OLK5JFETD7HB","created_at":"2026-07-05T01:56:59Z"},{"alias_kind":"pith_short_8","alias_value":"IXB6OLK5","created_at":"2026-07-05T01:56:59Z"}],"graph_snapshots":[{"event_id":"sha256:d07136772380a3409709ada178fa5a62e562889e86e526cb3084da85a1e4a9db","target":"graph","created_at":"2026-07-05T01:56:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2012.02447/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in which an aggregator orchestrates multiple parties to train a global model without sharing their training data. In this paper, we discuss causes of bias in FL and propose three pre-processing and in-processing methods to mitigate bias, without compromising data privacy, a key FL requirement. As data heterogeneity among parties is one of the challenging characteristics of FL, we conduct experiments over several data d","authors_text":"Annie Abay, Ebube Chuba, Heiko Ludwig, Nathalie Baracaldo, Shashank Rajamoni, Yi Zhou","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-04T08:04:12Z","title":"Mitigating Bias in Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.02447","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f57c482220b3eb5c2d5c239c5e241f6a0ae899ef7c3c79b1e759d7af21d84567","target":"record","created_at":"2026-07-05T01:56:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"044c87a4631191ff51fb1b8f14d9c0905a64415e1473563b7ecea68f0ab39a7d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-04T08:04:12Z","title_canon_sha256":"01fa11b6fc718b051f913bfe32e9dd6bc7c63901f5f831ab73db02700b14aeb5"},"schema_version":"1.0","source":{"id":"2012.02447","kind":"arxiv","version":1}},"canonical_sha256":"45c3e72d5d494931fce189e47763d4f41db2421677d27b4261f9c27d283a7470","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45c3e72d5d494931fce189e47763d4f41db2421677d27b4261f9c27d283a7470","first_computed_at":"2026-07-05T01:56:59.679970Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:56:59.679970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tYxuRc69DfKnHXKjAWSPogcWY/gwx7oblSnoN4gN2XGVZ8UN/JdC7/HeX0JkjcU2p7uVXxZqZ2cZEAML6K1YBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:56:59.680434Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.02447","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f57c482220b3eb5c2d5c239c5e241f6a0ae899ef7c3c79b1e759d7af21d84567","sha256:d07136772380a3409709ada178fa5a62e562889e86e526cb3084da85a1e4a9db"],"state_sha256":"55da65cdac8c06384f820268450fedb3fa8f476bbc9c1021b61ede4f22c3ec8f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UxwjlWJx1/n9e3IOSQFvg5VZQ/FdOTXfR5k3zMZZAgSz+wvBOHpoS/poONr7JUV+4SXlg2277aLlfLnYqHNQDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T01:16:12.519102Z","bundle_sha256":"6e471fbc6c93c689d1a114052aed905d67dfea4308f2eb4792d0244c27c4555c"}}