{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NXZPU6Y5JKH36T6FLJHSRVMEHC","short_pith_number":"pith:NXZPU6Y5","canonical_record":{"source":{"id":"2205.00470","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-01T14:01:33Z","cross_cats_sorted":["cs.CY","cs.GT"],"title_canon_sha256":"bed27d218fec05300b63763c8761e66bd7d488c7b2e13a0fb89ce2669ab47a15","abstract_canon_sha256":"ea14a1657f022eafa170c6a731dd32bdbf9abffc7ab142e9252dafc80b9993cf"},"schema_version":"1.0"},"canonical_sha256":"6df2fa7b1d4a8fbf4fc55a4f28d58438a929ef906bec8a72b5691d1b957ab77e","source":{"kind":"arxiv","id":"2205.00470","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.00470","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"arxiv_version","alias_value":"2205.00470v2","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.00470","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_12","alias_value":"NXZPU6Y5JKH3","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_16","alias_value":"NXZPU6Y5JKH36T6F","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_8","alias_value":"NXZPU6Y5","created_at":"2026-07-05T06:06:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NXZPU6Y5JKH36T6FLJHSRVMEHC","target":"record","payload":{"canonical_record":{"source":{"id":"2205.00470","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-01T14:01:33Z","cross_cats_sorted":["cs.CY","cs.GT"],"title_canon_sha256":"bed27d218fec05300b63763c8761e66bd7d488c7b2e13a0fb89ce2669ab47a15","abstract_canon_sha256":"ea14a1657f022eafa170c6a731dd32bdbf9abffc7ab142e9252dafc80b9993cf"},"schema_version":"1.0"},"canonical_sha256":"6df2fa7b1d4a8fbf4fc55a4f28d58438a929ef906bec8a72b5691d1b957ab77e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:28.449171Z","signature_b64":"KXxF6wrhziLYmH3pK2CDlaUugK7mjLNCCjIGUegsnLAxLFBBxbgSNrHdTq2CpjVN+yJgpbV31OtRC+VfLThdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6df2fa7b1d4a8fbf4fc55a4f28d58438a929ef906bec8a72b5691d1b957ab77e","last_reissued_at":"2026-07-05T06:06:28.448688Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:28.448688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.00470","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:06:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jmHjClm6jxlWY3HP38ZZTB8iPjbhk/5SczBJJ9qIkpAr7WWqXf3rF6EU+kNKRxhpUFxMtpIYiw89n5Fc2dWIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:27:04.163435Z"},"content_sha256":"1e242dae2ab04b5437c7abc69cfa79cdb7ebfe4179423b3466d2379b5c20c46a","schema_version":"1.0","event_id":"sha256:1e242dae2ab04b5437c7abc69cfa79cdb7ebfe4179423b3466d2379b5c20c46a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NXZPU6Y5JKH36T6FLJHSRVMEHC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reward Systems for Trustworthy Medical Federated Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.GT"],"primary_cat":"cs.LG","authors_text":"Ali Sunyaev, Florian Leiser, Konstantin D. Pandl, Scott Thiebes","submitted_at":"2022-05-01T14:01:33Z","abstract_excerpt":"Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models. In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contrib"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.00470","kind":"arxiv","version":2},"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/2205.00470/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":"2026-07-05T06:06:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qoy9j5JLtPHhcyeyWFDyyICK0+q+FrvTrV4yiJKqTAsVFwJAIQX144ZdGR5kLSTFaFwAt27QDKsgtwCmKgO5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:27:04.163955Z"},"content_sha256":"ec40c6ec8daa0f5c3a0bf1a434728d507db70e5eaaf94777ee6e7b30b10b3dfe","schema_version":"1.0","event_id":"sha256:ec40c6ec8daa0f5c3a0bf1a434728d507db70e5eaaf94777ee6e7b30b10b3dfe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/bundle.json","state_url":"https://pith.science/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/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":"2026-08-09T15:27:04Z","links":{"resolver":"https://pith.science/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC","bundle":"https://pith.science/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/bundle.json","state":"https://pith.science/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NXZPU6Y5JKH36T6FLJHSRVMEHC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NXZPU6Y5JKH36T6FLJHSRVMEHC","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":"ea14a1657f022eafa170c6a731dd32bdbf9abffc7ab142e9252dafc80b9993cf","cross_cats_sorted":["cs.CY","cs.GT"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-01T14:01:33Z","title_canon_sha256":"bed27d218fec05300b63763c8761e66bd7d488c7b2e13a0fb89ce2669ab47a15"},"schema_version":"1.0","source":{"id":"2205.00470","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.00470","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"arxiv_version","alias_value":"2205.00470v2","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.00470","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_12","alias_value":"NXZPU6Y5JKH3","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_16","alias_value":"NXZPU6Y5JKH36T6F","created_at":"2026-07-05T06:06:28Z"},{"alias_kind":"pith_short_8","alias_value":"NXZPU6Y5","created_at":"2026-07-05T06:06:28Z"}],"graph_snapshots":[{"event_id":"sha256:ec40c6ec8daa0f5c3a0bf1a434728d507db70e5eaaf94777ee6e7b30b10b3dfe","target":"graph","created_at":"2026-07-05T06:06:28Z","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/2205.00470/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models. In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contrib","authors_text":"Ali Sunyaev, Florian Leiser, Konstantin D. Pandl, Scott Thiebes","cross_cats":["cs.CY","cs.GT"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-01T14:01:33Z","title":"Reward Systems for Trustworthy Medical Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.00470","kind":"arxiv","version":2},"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":"sha256:1e242dae2ab04b5437c7abc69cfa79cdb7ebfe4179423b3466d2379b5c20c46a","target":"record","created_at":"2026-07-05T06:06:28Z","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":"ea14a1657f022eafa170c6a731dd32bdbf9abffc7ab142e9252dafc80b9993cf","cross_cats_sorted":["cs.CY","cs.GT"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-01T14:01:33Z","title_canon_sha256":"bed27d218fec05300b63763c8761e66bd7d488c7b2e13a0fb89ce2669ab47a15"},"schema_version":"1.0","source":{"id":"2205.00470","kind":"arxiv","version":2}},"canonical_sha256":"6df2fa7b1d4a8fbf4fc55a4f28d58438a929ef906bec8a72b5691d1b957ab77e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6df2fa7b1d4a8fbf4fc55a4f28d58438a929ef906bec8a72b5691d1b957ab77e","first_computed_at":"2026-07-05T06:06:28.448688Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:06:28.448688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KXxF6wrhziLYmH3pK2CDlaUugK7mjLNCCjIGUegsnLAxLFBBxbgSNrHdTq2CpjVN+yJgpbV31OtRC+VfLThdBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:06:28.449171Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.00470","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1e242dae2ab04b5437c7abc69cfa79cdb7ebfe4179423b3466d2379b5c20c46a","sha256:ec40c6ec8daa0f5c3a0bf1a434728d507db70e5eaaf94777ee6e7b30b10b3dfe"],"state_sha256":"b3b526198876eeb658645b47472e94a7bd1da39832af5fa7dc283f03107e6a86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"551R5QJQfT8ezCLSjzZlOW7xztpEVVoJgien4xqR0ztnlgtkDv1l/X6LmcbB+YEtO648PsSpgKx3joXJz7gcBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T15:27:04.168351Z","bundle_sha256":"2c69d15531f72856545e667d4028b6543048e54c6f086cd4396db4a27263e5ec"}}