{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VSBFEURUWA5SQHLRLB7QBXTLJO","short_pith_number":"pith:VSBFEURU","canonical_record":{"source":{"id":"2502.16832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-24T04:35:48Z","cross_cats_sorted":[],"title_canon_sha256":"ce7ed568a429c21947dd859e3494ac17b93404452191f36c68fffdde291f77be","abstract_canon_sha256":"b7b2ff8f3cc41389a9e6bb45e89b1986f5daf0b069cb44d8ca20dd80d19fce62"},"schema_version":"1.0"},"canonical_sha256":"ac82525234b03b281d71587f00de6b4b90edadfc6a7d370589401acfb0f03c36","source":{"kind":"arxiv","id":"2502.16832","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.16832","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"arxiv_version","alias_value":"2502.16832v1","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16832","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_12","alias_value":"VSBFEURUWA5S","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_16","alias_value":"VSBFEURUWA5SQHLR","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_8","alias_value":"VSBFEURU","created_at":"2026-07-05T10:19:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VSBFEURUWA5SQHLRLB7QBXTLJO","target":"record","payload":{"canonical_record":{"source":{"id":"2502.16832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-24T04:35:48Z","cross_cats_sorted":[],"title_canon_sha256":"ce7ed568a429c21947dd859e3494ac17b93404452191f36c68fffdde291f77be","abstract_canon_sha256":"b7b2ff8f3cc41389a9e6bb45e89b1986f5daf0b069cb44d8ca20dd80d19fce62"},"schema_version":"1.0"},"canonical_sha256":"ac82525234b03b281d71587f00de6b4b90edadfc6a7d370589401acfb0f03c36","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:00.380534Z","signature_b64":"waEKi8+DZRDUaVqzsbXmWCijWS2tvMXqioVyZMwPA6DrAsvENZxU2RsQupvcNXQNMnJNMpyfPbLEGF7jdIOvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac82525234b03b281d71587f00de6b4b90edadfc6a7d370589401acfb0f03c36","last_reissued_at":"2026-07-05T10:19:00.378570Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:00.378570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.16832","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":"2026-07-05T10:19:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UJP5hz0J3VeTcv7D97lbV3uvbM8FruQGtQ4lqChWjWKoyySVWcZfoYWbinnzqzGrXEkAzbL4pN3fmTCqWkO+DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:05:54.717438Z"},"content_sha256":"0179f5957d1319019d4dba2e35f937dd1ce6c3c24f114c3108708056ea9c815b","schema_version":"1.0","event_id":"sha256:0179f5957d1319019d4dba2e35f937dd1ce6c3c24f114c3108708056ea9c815b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VSBFEURUWA5SQHLRLB7QBXTLJO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Liu, Meilu Zhu, Qiushi Yang, Yixuan Yuan, Zhifan Gao","submitted_at":"2025-02-24T04:35:48Z","abstract_excerpt":"Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have shown that data heterogeneity incurs local learning bias in classifiers and feature extractors of client models during local training, leading to the performance degradation of a federation system. To address these issues, we propose a novel framework called Federated Bias eliMinating (FedBM) to get rid of local learning bias in heterogeneous federated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16832","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/2502.16832/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":"2026-07-05T10:19:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oHDkj+ldLf/j0TrR1z9ruS9/XMliE6DBwRRInANdPlkQmchu9fKDrIcY/Xo0s+QcLoNch2kqywU/bsTarUkmBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:05:54.717975Z"},"content_sha256":"a5e35f2d13ee6d13b409b2ebb0d81ca952aa49330738725189620060f510ed95","schema_version":"1.0","event_id":"sha256:a5e35f2d13ee6d13b409b2ebb0d81ca952aa49330738725189620060f510ed95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/bundle.json","state_url":"https://pith.science/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/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":"2026-08-12T19:05:54Z","links":{"resolver":"https://pith.science/pith/VSBFEURUWA5SQHLRLB7QBXTLJO","bundle":"https://pith.science/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/bundle.json","state":"https://pith.science/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VSBFEURUWA5SQHLRLB7QBXTLJO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VSBFEURUWA5SQHLRLB7QBXTLJO","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":"b7b2ff8f3cc41389a9e6bb45e89b1986f5daf0b069cb44d8ca20dd80d19fce62","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-24T04:35:48Z","title_canon_sha256":"ce7ed568a429c21947dd859e3494ac17b93404452191f36c68fffdde291f77be"},"schema_version":"1.0","source":{"id":"2502.16832","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.16832","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"arxiv_version","alias_value":"2502.16832v1","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16832","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_12","alias_value":"VSBFEURUWA5S","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_16","alias_value":"VSBFEURUWA5SQHLR","created_at":"2026-07-05T10:19:00Z"},{"alias_kind":"pith_short_8","alias_value":"VSBFEURU","created_at":"2026-07-05T10:19:00Z"}],"graph_snapshots":[{"event_id":"sha256:a5e35f2d13ee6d13b409b2ebb0d81ca952aa49330738725189620060f510ed95","target":"graph","created_at":"2026-07-05T10:19:00Z","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/2502.16832/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have shown that data heterogeneity incurs local learning bias in classifiers and feature extractors of client models during local training, leading to the performance degradation of a federation system. To address these issues, we propose a novel framework called Federated Bias eliMinating (FedBM) to get rid of local learning bias in heterogeneous federated","authors_text":"Jun Liu, Meilu Zhu, Qiushi Yang, Yixuan Yuan, Zhifan Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-24T04:35:48Z","title":"FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16832","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":"sha256:0179f5957d1319019d4dba2e35f937dd1ce6c3c24f114c3108708056ea9c815b","target":"record","created_at":"2026-07-05T10:19:00Z","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":"b7b2ff8f3cc41389a9e6bb45e89b1986f5daf0b069cb44d8ca20dd80d19fce62","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-24T04:35:48Z","title_canon_sha256":"ce7ed568a429c21947dd859e3494ac17b93404452191f36c68fffdde291f77be"},"schema_version":"1.0","source":{"id":"2502.16832","kind":"arxiv","version":1}},"canonical_sha256":"ac82525234b03b281d71587f00de6b4b90edadfc6a7d370589401acfb0f03c36","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac82525234b03b281d71587f00de6b4b90edadfc6a7d370589401acfb0f03c36","first_computed_at":"2026-07-05T10:19:00.378570Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:19:00.378570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"waEKi8+DZRDUaVqzsbXmWCijWS2tvMXqioVyZMwPA6DrAsvENZxU2RsQupvcNXQNMnJNMpyfPbLEGF7jdIOvDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:19:00.380534Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.16832","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0179f5957d1319019d4dba2e35f937dd1ce6c3c24f114c3108708056ea9c815b","sha256:a5e35f2d13ee6d13b409b2ebb0d81ca952aa49330738725189620060f510ed95"],"state_sha256":"8c0555d31237f8d06e2815c804e72f2eaf6b53d7f184080e5fed3de6f45d9ba0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GsxQUT77yjTeBHEiY7fx9gBoPtwsK8jLVfyuoQkCSwtSXguHjfQPFIEo9rJAivB5c7egQv6ReUW2GJ/LU8JsDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T19:05:54.722242Z","bundle_sha256":"ad05860b7f739fb4c3efc27e2c28950e0cf9854511929ba1b1d12687855517f7"}}