{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SPQDEQZ2PAXMHR7NYMCWUZKNEA","short_pith_number":"pith:SPQDEQZ2","schema_version":"1.0","canonical_sha256":"93e032433a782ec3c7edc3056a654d2023ee2814a7b5dd199e4ace847654ab49","source":{"kind":"arxiv","id":"2208.10919","version":1},"attestation_state":"computed","paper":{"title":"Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"H.R. Tizhoosh, Milad Sikaroudi, Morteza Babaei, S. Maryam Hosseini","submitted_at":"2022-08-21T23:56:28Z","abstract_excerpt":"Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each h"},"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":"2208.10919","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-08-21T23:56:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"49a51cc78774335ed086f0247d0d8f0cfea2309aae6723f44a0a90998a754499","abstract_canon_sha256":"46509b03cf6d37d473b0a8200fcb08db5983a8e42dbfb7c111e03cde39ccf7c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:50:41.894179Z","signature_b64":"Gk4h0gDVaCadpHIEGqYV8Ybv9O4t9k+6mu2+w6qFRljVw7kIEpY0+81aN0xgbtEUKawxNAEgzTnbdu68KpCRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93e032433a782ec3c7edc3056a654d2023ee2814a7b5dd199e4ace847654ab49","last_reissued_at":"2026-07-05T04:50:41.893673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:50:41.893673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"H.R. Tizhoosh, Milad Sikaroudi, Morteza Babaei, S. Maryam Hosseini","submitted_at":"2022-08-21T23:56:28Z","abstract_excerpt":"Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.10919","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/2208.10919/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":"2208.10919","created_at":"2026-07-05T04:50:41.893735+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.10919v1","created_at":"2026-07-05T04:50:41.893735+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.10919","created_at":"2026-07-05T04:50:41.893735+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPQDEQZ2PAXM","created_at":"2026-07-05T04:50:41.893735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPQDEQZ2PAXMHR7N","created_at":"2026-07-05T04:50:41.893735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPQDEQZ2","created_at":"2026-07-05T04:50:41.893735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.14205","citing_title":"Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA","json":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA.json","graph_json":"https://pith.science/api/pith-number/SPQDEQZ2PAXMHR7NYMCWUZKNEA/graph.json","events_json":"https://pith.science/api/pith-number/SPQDEQZ2PAXMHR7NYMCWUZKNEA/events.json","paper":"https://pith.science/paper/SPQDEQZ2"},"agent_actions":{"view_html":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA","download_json":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA.json","view_paper":"https://pith.science/paper/SPQDEQZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.10919&json=true","fetch_graph":"https://pith.science/api/pith-number/SPQDEQZ2PAXMHR7NYMCWUZKNEA/graph.json","fetch_events":"https://pith.science/api/pith-number/SPQDEQZ2PAXMHR7NYMCWUZKNEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA/action/storage_attestation","attest_author":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA/action/author_attestation","sign_citation":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA/action/citation_signature","submit_replication":"https://pith.science/pith/SPQDEQZ2PAXMHR7NYMCWUZKNEA/action/replication_record"}},"created_at":"2026-07-05T04:50:41.893735+00:00","updated_at":"2026-07-05T04:50:41.893735+00:00"}