{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:47EAWPA2CTFAZDNTMBJSGUHIT3","short_pith_number":"pith:47EAWPA2","schema_version":"1.0","canonical_sha256":"e7c80b3c1a14ca0c8db360532350e89efb6bab8f23b8f259afcac006df74eb48","source":{"kind":"arxiv","id":"2506.17562","version":2},"attestation_state":"computed","paper":{"title":"LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Cheng Jin, Haibo Jin, Hao Chen, Haoxuan Che, Yi Lin, Zhengrui Guo","submitted_at":"2025-06-21T03:13:08Z","abstract_excerpt":"LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scattered across multiple centers. Centralizing these data is exceptionally challenging due to privacy regulations, thereby impeding model development and broader adoption of LLM-driven MRG models. To address this challenge, we present FedMRG, the first framework that leverages Federated Learning (FL) to enable privacy-preserving, multi-center development of LLM-driven MRG models, specifically designed to overcome the cr"},"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":"2506.17562","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-21T03:13:08Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"5406fe589bf996e7c13f097c251552a6d66faed710fd991f3718555e7752b8b2","abstract_canon_sha256":"bf239b977345a8f5e8b4d5a04abaabf2256cd64bc0a0ff6822548156c43addc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:12.457120Z","signature_b64":"m6xrrzPhqXwt5HhWNCoSWAbmKWpQrY+CYDEuuaCoNZjqUTA7vIU1Evl/euQ7Pkf1kS7sFE8EnnZt66538RXrAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7c80b3c1a14ca0c8db360532350e89efb6bab8f23b8f259afcac006df74eb48","last_reissued_at":"2026-07-05T11:39:12.456657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:12.456657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Cheng Jin, Haibo Jin, Hao Chen, Haoxuan Che, Yi Lin, Zhengrui Guo","submitted_at":"2025-06-21T03:13:08Z","abstract_excerpt":"LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scattered across multiple centers. Centralizing these data is exceptionally challenging due to privacy regulations, thereby impeding model development and broader adoption of LLM-driven MRG models. To address this challenge, we present FedMRG, the first framework that leverages Federated Learning (FL) to enable privacy-preserving, multi-center development of LLM-driven MRG models, specifically designed to overcome the cr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17562","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/2506.17562/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":"2506.17562","created_at":"2026-07-05T11:39:12.456713+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17562v2","created_at":"2026-07-05T11:39:12.456713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17562","created_at":"2026-07-05T11:39:12.456713+00:00"},{"alias_kind":"pith_short_12","alias_value":"47EAWPA2CTFA","created_at":"2026-07-05T11:39:12.456713+00:00"},{"alias_kind":"pith_short_16","alias_value":"47EAWPA2CTFAZDNT","created_at":"2026-07-05T11:39:12.456713+00:00"},{"alias_kind":"pith_short_8","alias_value":"47EAWPA2","created_at":"2026-07-05T11:39:12.456713+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/47EAWPA2CTFAZDNTMBJSGUHIT3","json":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3.json","graph_json":"https://pith.science/api/pith-number/47EAWPA2CTFAZDNTMBJSGUHIT3/graph.json","events_json":"https://pith.science/api/pith-number/47EAWPA2CTFAZDNTMBJSGUHIT3/events.json","paper":"https://pith.science/paper/47EAWPA2"},"agent_actions":{"view_html":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3","download_json":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3.json","view_paper":"https://pith.science/paper/47EAWPA2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17562&json=true","fetch_graph":"https://pith.science/api/pith-number/47EAWPA2CTFAZDNTMBJSGUHIT3/graph.json","fetch_events":"https://pith.science/api/pith-number/47EAWPA2CTFAZDNTMBJSGUHIT3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3/action/storage_attestation","attest_author":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3/action/author_attestation","sign_citation":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3/action/citation_signature","submit_replication":"https://pith.science/pith/47EAWPA2CTFAZDNTMBJSGUHIT3/action/replication_record"}},"created_at":"2026-07-05T11:39:12.456713+00:00","updated_at":"2026-07-05T11:39:12.456713+00:00"}