{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DB2YQDPOUJCI6BI53356E6GC7K","short_pith_number":"pith:DB2YQDPO","schema_version":"1.0","canonical_sha256":"1875880deea2448f051ddefbe278c2faabeb8f91d3ac279617da9d34609da622","source":{"kind":"arxiv","id":"2401.03609","version":3},"attestation_state":"computed","paper":{"title":"Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kasra Borazjani, Leslie Ying, Naji Khosravan, Seyyedali Hosseinalipour","submitted_at":"2024-01-07T23:45:01Z","abstract_excerpt":"The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging.\n  However, existing works on multi-modal FL often presume that all data-collecting institutions have access to all data modal"},"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":"2401.03609","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-07T23:45:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d6ca9a698bf158c808b303cf25a5054054c675c0ce697adf012a158750a29783","abstract_canon_sha256":"32e83d218747d67201a3e84863d4a0384b6fb97ecc5bd0e9dc7cc2b768aab9cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:36.376253Z","signature_b64":"fy2BgcObExV9686mOlRN/8fu7zW+sdC0hN3ksOB+AyxHwFC+cEDwAqvWhkbZjFChhpUnokp8Lon7BzUMNa84DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1875880deea2448f051ddefbe278c2faabeb8f91d3ac279617da9d34609da622","last_reissued_at":"2026-07-05T09:17:36.375765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:36.375765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kasra Borazjani, Leslie Ying, Naji Khosravan, Seyyedali Hosseinalipour","submitted_at":"2024-01-07T23:45:01Z","abstract_excerpt":"The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging.\n  However, existing works on multi-modal FL often presume that all data-collecting institutions have access to all data modal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03609","kind":"arxiv","version":3},"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/2401.03609/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":"2401.03609","created_at":"2026-07-05T09:17:36.375827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.03609v3","created_at":"2026-07-05T09:17:36.375827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03609","created_at":"2026-07-05T09:17:36.375827+00:00"},{"alias_kind":"pith_short_12","alias_value":"DB2YQDPOUJCI","created_at":"2026-07-05T09:17:36.375827+00:00"},{"alias_kind":"pith_short_16","alias_value":"DB2YQDPOUJCI6BI5","created_at":"2026-07-05T09:17:36.375827+00:00"},{"alias_kind":"pith_short_8","alias_value":"DB2YQDPO","created_at":"2026-07-05T09:17:36.375827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.12377","citing_title":"Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions","ref_index":89,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K","json":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K.json","graph_json":"https://pith.science/api/pith-number/DB2YQDPOUJCI6BI53356E6GC7K/graph.json","events_json":"https://pith.science/api/pith-number/DB2YQDPOUJCI6BI53356E6GC7K/events.json","paper":"https://pith.science/paper/DB2YQDPO"},"agent_actions":{"view_html":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K","download_json":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K.json","view_paper":"https://pith.science/paper/DB2YQDPO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.03609&json=true","fetch_graph":"https://pith.science/api/pith-number/DB2YQDPOUJCI6BI53356E6GC7K/graph.json","fetch_events":"https://pith.science/api/pith-number/DB2YQDPOUJCI6BI53356E6GC7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K/action/storage_attestation","attest_author":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K/action/author_attestation","sign_citation":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K/action/citation_signature","submit_replication":"https://pith.science/pith/DB2YQDPOUJCI6BI53356E6GC7K/action/replication_record"}},"created_at":"2026-07-05T09:17:36.375827+00:00","updated_at":"2026-07-05T09:17:36.375827+00:00"}