{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3GKNOMLVSFXDJ37VPZKKS6ILKF","short_pith_number":"pith:3GKNOMLV","schema_version":"1.0","canonical_sha256":"d994d73175916e34eff57e54a9790b516b872c96064e2c2dd3d18ef02347c1e7","source":{"kind":"arxiv","id":"2607.13386","version":1},"attestation_state":"computed","paper":{"title":"FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shengchao Chen, Ting Shu","submitted_at":"2026-07-15T02:28:46Z","abstract_excerpt":"Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or train from scratch within a single modality, lacking the flexibility to unify tasks. We identify an under-explored challenge, Imaging Modality Heterogeneity, where clients operate under two structural regimes: Overlapped (shared modalities with heterogeneous label distributions) and Non-overlapped (fully disjoint modalities per client). "},"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":"2607.13386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-15T02:28:46Z","cross_cats_sorted":[],"title_canon_sha256":"e14d14c10e9f09d2dc0b50028e6d6e553e6dbddfe4b64f750a46c2c77e03cb75","abstract_canon_sha256":"b67f1591911c9fe5b04cb6f91005b2c30d282dbeb21e0e9580a6b8db633e7357"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T00:22:15.103197Z","signature_b64":"tBxiMkTDF+07bCcC58nqq7qCaC+9iW8k1fnBCr4SVDHCFx2FaZydJDgfpKeF0Gu7opBa0tWLLAtL8uWE+MRBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d994d73175916e34eff57e54a9790b516b872c96064e2c2dd3d18ef02347c1e7","last_reissued_at":"2026-07-16T00:22:15.102249Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T00:22:15.102249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shengchao Chen, Ting Shu","submitted_at":"2026-07-15T02:28:46Z","abstract_excerpt":"Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or train from scratch within a single modality, lacking the flexibility to unify tasks. We identify an under-explored challenge, Imaging Modality Heterogeneity, where clients operate under two structural regimes: Overlapped (shared modalities with heterogeneous label distributions) and Non-overlapped (fully disjoint modalities per client). "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13386","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/2607.13386/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":"2607.13386","created_at":"2026-07-16T00:22:15.102755+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13386v1","created_at":"2026-07-16T00:22:15.102755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13386","created_at":"2026-07-16T00:22:15.102755+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GKNOMLVSFXD","created_at":"2026-07-16T00:22:15.102755+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GKNOMLVSFXDJ37V","created_at":"2026-07-16T00:22:15.102755+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GKNOMLV","created_at":"2026-07-16T00:22:15.102755+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/3GKNOMLVSFXDJ37VPZKKS6ILKF","json":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF.json","graph_json":"https://pith.science/api/pith-number/3GKNOMLVSFXDJ37VPZKKS6ILKF/graph.json","events_json":"https://pith.science/api/pith-number/3GKNOMLVSFXDJ37VPZKKS6ILKF/events.json","paper":"https://pith.science/paper/3GKNOMLV"},"agent_actions":{"view_html":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF","download_json":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF.json","view_paper":"https://pith.science/paper/3GKNOMLV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13386&json=true","fetch_graph":"https://pith.science/api/pith-number/3GKNOMLVSFXDJ37VPZKKS6ILKF/graph.json","fetch_events":"https://pith.science/api/pith-number/3GKNOMLVSFXDJ37VPZKKS6ILKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF/action/storage_attestation","attest_author":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF/action/author_attestation","sign_citation":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF/action/citation_signature","submit_replication":"https://pith.science/pith/3GKNOMLVSFXDJ37VPZKKS6ILKF/action/replication_record"}},"created_at":"2026-07-16T00:22:15.102755+00:00","updated_at":"2026-07-16T00:22:15.102755+00:00"}