{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ETFPRHWQ2KKQOH5LJ7BKTTWZWD","short_pith_number":"pith:ETFPRHWQ","canonical_record":{"source":{"id":"2211.07300","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-14T12:24:00Z","cross_cats_sorted":[],"title_canon_sha256":"a15c44acbf7915a05b95292da152d30b7d4d9f4a089cf1873d1b76aff9e3ffc1","abstract_canon_sha256":"b6740199da36b6b0fd78923b51bc677206368413d919a3d790738b333c51eaf0"},"schema_version":"1.0"},"canonical_sha256":"24caf89ed0d295071fab4fc2a9ced9b0c361862f7db9ed6727b9984a58da43fb","source":{"kind":"arxiv","id":"2211.07300","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07300","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07300v1","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07300","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_12","alias_value":"ETFPRHWQ2KKQ","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_16","alias_value":"ETFPRHWQ2KKQOH5L","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_8","alias_value":"ETFPRHWQ","created_at":"2026-07-05T05:15:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ETFPRHWQ2KKQOH5LJ7BKTTWZWD","target":"record","payload":{"canonical_record":{"source":{"id":"2211.07300","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-14T12:24:00Z","cross_cats_sorted":[],"title_canon_sha256":"a15c44acbf7915a05b95292da152d30b7d4d9f4a089cf1873d1b76aff9e3ffc1","abstract_canon_sha256":"b6740199da36b6b0fd78923b51bc677206368413d919a3d790738b333c51eaf0"},"schema_version":"1.0"},"canonical_sha256":"24caf89ed0d295071fab4fc2a9ced9b0c361862f7db9ed6727b9984a58da43fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:51.918592Z","signature_b64":"bmCg4K7096Nsz/WQcG7hHsX4nRr7Csxvx67mIul+EYCH3dzP0RYMv+Fs1Izpgkt9d6ICyJKJx6go2hj94b1JDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24caf89ed0d295071fab4fc2a9ced9b0c361862f7db9ed6727b9984a58da43fb","last_reissued_at":"2026-07-05T05:15:51.918213Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:51.918213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.07300","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":"2026-07-05T05:15:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vmh1VydQkPdhZHr14P0949Td5Bg7z0C+33nm/Sa386u7whkjTCA3x1YdFt/39+fIM39rKTsVziF9C0s4FCsuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T15:40:25.968302Z"},"content_sha256":"f47a345d23b494f0b65a18e58b039a6b27659605e329c4ad1281f1c6d51b08e0","schema_version":"1.0","event_id":"sha256:f47a345d23b494f0b65a18e58b039a6b27659605e329c4ad1281f1c6d51b08e0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ETFPRHWQ2KKQOH5LJ7BKTTWZWD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Universal EHR Federated Learning Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward Choi, Junu Kim, Kyunghoon Hur, Seongjun Yang","submitted_at":"2022-11-14T12:24:00Z","abstract_excerpt":"Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We attempt to address both the challenges simultaneously by combining UniHPF and FL. Our study is the first approach to unify heterogeneous EHRs into a single FL framework. This combination provides an average of 3.4"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07300","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/2211.07300/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":"2026-07-05T05:15:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTWgHrehaSWEvT9pIG1oIqjYxckVPH40cKnYClp+5o6pcdS4SyoDKa9EJxg4yIj+GoxXaPz3rHaDi8HbXcN+AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T15:40:25.968840Z"},"content_sha256":"14609b9ab539523e8bb982ab2f4906e85eea3cda701982b52d67579a18e0293b","schema_version":"1.0","event_id":"sha256:14609b9ab539523e8bb982ab2f4906e85eea3cda701982b52d67579a18e0293b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/bundle.json","state_url":"https://pith.science/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/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":"2026-07-31T15:40:25Z","links":{"resolver":"https://pith.science/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD","bundle":"https://pith.science/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/bundle.json","state":"https://pith.science/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ETFPRHWQ2KKQOH5LJ7BKTTWZWD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ETFPRHWQ2KKQOH5LJ7BKTTWZWD","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":"b6740199da36b6b0fd78923b51bc677206368413d919a3d790738b333c51eaf0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-14T12:24:00Z","title_canon_sha256":"a15c44acbf7915a05b95292da152d30b7d4d9f4a089cf1873d1b76aff9e3ffc1"},"schema_version":"1.0","source":{"id":"2211.07300","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07300","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07300v1","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07300","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_12","alias_value":"ETFPRHWQ2KKQ","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_16","alias_value":"ETFPRHWQ2KKQOH5L","created_at":"2026-07-05T05:15:51Z"},{"alias_kind":"pith_short_8","alias_value":"ETFPRHWQ","created_at":"2026-07-05T05:15:51Z"}],"graph_snapshots":[{"event_id":"sha256:14609b9ab539523e8bb982ab2f4906e85eea3cda701982b52d67579a18e0293b","target":"graph","created_at":"2026-07-05T05:15:51Z","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/2211.07300/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We attempt to address both the challenges simultaneously by combining UniHPF and FL. Our study is the first approach to unify heterogeneous EHRs into a single FL framework. This combination provides an average of 3.4","authors_text":"Edward Choi, Junu Kim, Kyunghoon Hur, Seongjun Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-14T12:24:00Z","title":"Universal EHR Federated Learning Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07300","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:f47a345d23b494f0b65a18e58b039a6b27659605e329c4ad1281f1c6d51b08e0","target":"record","created_at":"2026-07-05T05:15:51Z","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":"b6740199da36b6b0fd78923b51bc677206368413d919a3d790738b333c51eaf0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-14T12:24:00Z","title_canon_sha256":"a15c44acbf7915a05b95292da152d30b7d4d9f4a089cf1873d1b76aff9e3ffc1"},"schema_version":"1.0","source":{"id":"2211.07300","kind":"arxiv","version":1}},"canonical_sha256":"24caf89ed0d295071fab4fc2a9ced9b0c361862f7db9ed6727b9984a58da43fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24caf89ed0d295071fab4fc2a9ced9b0c361862f7db9ed6727b9984a58da43fb","first_computed_at":"2026-07-05T05:15:51.918213Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:15:51.918213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bmCg4K7096Nsz/WQcG7hHsX4nRr7Csxvx67mIul+EYCH3dzP0RYMv+Fs1Izpgkt9d6ICyJKJx6go2hj94b1JDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:15:51.918592Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.07300","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f47a345d23b494f0b65a18e58b039a6b27659605e329c4ad1281f1c6d51b08e0","sha256:14609b9ab539523e8bb982ab2f4906e85eea3cda701982b52d67579a18e0293b"],"state_sha256":"b34aaf69d1d474cd49c8b2c01c022dc51c2e22266191c85b2ed307d13d73952c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0nYGZppqEcV+J974sLx0F5e9DkvZdAin2qZ+imfCQVdDDHhmc7wprYrrf8Fw75V8lP3U2PBOOm2Wra0wDQzCCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T15:40:25.973973Z","bundle_sha256":"85509da5c375ca0d6149b118a6db1f08a881def5f9510f38632d512bb356ac81"}}