{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AUJKMYFZLU2XTS3LWCVKVS45NY","short_pith_number":"pith:AUJKMYFZ","schema_version":"1.0","canonical_sha256":"0512a660b95d3579cb6bb0aaaacb9d6e1b5a11714021d559d7b44bb97396dd4d","source":{"kind":"arxiv","id":"1911.12560","version":1},"attestation_state":"computed","paper":{"title":"Free-riders in Federated Learning: Attacks and Defenses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jian Liu, Jierui Lin, Min Du","submitted_at":"2019-11-28T07:13:48Z","abstract_excerpt":"Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the "},"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":"1911.12560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-28T07:13:48Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"f16a60ed443ef6fbf621b5d0551c29751574741076b74f7e2ccdc5120593c609","abstract_canon_sha256":"69e8510d20494ff444acceee9c14f39c0f01df6fa959798400d1931e16082a83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:22:46.641703Z","signature_b64":"CaH3FxKHqItcqXV0hLgLsJtHBAgnw0zWIsAtIeP4sX7oegbHxt9qfn5llTXsSDYLiwJh+sZhuSMxB2wLz/BUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0512a660b95d3579cb6bb0aaaacb9d6e1b5a11714021d559d7b44bb97396dd4d","last_reissued_at":"2026-07-05T00:22:46.641307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:22:46.641307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Free-riders in Federated Learning: Attacks and Defenses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jian Liu, Jierui Lin, Min Du","submitted_at":"2019-11-28T07:13:48Z","abstract_excerpt":"Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.12560","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/1911.12560/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":"1911.12560","created_at":"2026-07-05T00:22:46.641375+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.12560v1","created_at":"2026-07-05T00:22:46.641375+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.12560","created_at":"2026-07-05T00:22:46.641375+00:00"},{"alias_kind":"pith_short_12","alias_value":"AUJKMYFZLU2X","created_at":"2026-07-05T00:22:46.641375+00:00"},{"alias_kind":"pith_short_16","alias_value":"AUJKMYFZLU2XTS3L","created_at":"2026-07-05T00:22:46.641375+00:00"},{"alias_kind":"pith_short_8","alias_value":"AUJKMYFZ","created_at":"2026-07-05T00:22:46.641375+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06987","citing_title":"Response Time Enhances Alignment with Heterogeneous Preferences","ref_index":212,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04611","citing_title":"Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY","json":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY.json","graph_json":"https://pith.science/api/pith-number/AUJKMYFZLU2XTS3LWCVKVS45NY/graph.json","events_json":"https://pith.science/api/pith-number/AUJKMYFZLU2XTS3LWCVKVS45NY/events.json","paper":"https://pith.science/paper/AUJKMYFZ"},"agent_actions":{"view_html":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY","download_json":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY.json","view_paper":"https://pith.science/paper/AUJKMYFZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.12560&json=true","fetch_graph":"https://pith.science/api/pith-number/AUJKMYFZLU2XTS3LWCVKVS45NY/graph.json","fetch_events":"https://pith.science/api/pith-number/AUJKMYFZLU2XTS3LWCVKVS45NY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY/action/storage_attestation","attest_author":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY/action/author_attestation","sign_citation":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY/action/citation_signature","submit_replication":"https://pith.science/pith/AUJKMYFZLU2XTS3LWCVKVS45NY/action/replication_record"}},"created_at":"2026-07-05T00:22:46.641375+00:00","updated_at":"2026-07-05T00:22:46.641375+00:00"}