{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4WHFA4G2B2SKVDNCPJIN4JEA56","short_pith_number":"pith:4WHFA4G2","schema_version":"1.0","canonical_sha256":"e58e5070da0ea4aa8da27a50de2480ef8fd9fcdbe6254af94bc833c0cde4d237","source":{"kind":"arxiv","id":"2310.07268","version":1},"attestation_state":"computed","paper":{"title":"RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Guo, Changan Yang, Helei Cui, Yao Zhang, Yaxing Chen, Zheng Yan, Zhiwen Yu, Zijiang Yang","submitted_at":"2023-10-11T07:50:51Z","abstract_excerpt":"Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI"},"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":"2310.07268","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-11T07:50:51Z","cross_cats_sorted":[],"title_canon_sha256":"e54de038687ea6c9215da64b9b4d9e3c4896dba39582b0c4e02bee959e42179f","abstract_canon_sha256":"b1e0e9ab5ed0a2294ec98064c6b64f62349eaa0b9b7a2330d4a17259ebee384c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:42.914682Z","signature_b64":"i+Z6ZbAG6VCqfhu4smJbEjf7kXnODohtVk3wSH52oKiIrQgUjzpwJpn8it/pAWebt0utp+dFa3O4Y3nKmjT8Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e58e5070da0ea4aa8da27a50de2480ef8fd9fcdbe6254af94bc833c0cde4d237","last_reissued_at":"2026-07-05T06:59:42.914205Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:42.914205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Guo, Changan Yang, Helei Cui, Yao Zhang, Yaxing Chen, Zheng Yan, Zhiwen Yu, Zijiang Yang","submitted_at":"2023-10-11T07:50:51Z","abstract_excerpt":"Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.07268","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/2310.07268/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":"2310.07268","created_at":"2026-07-05T06:59:42.914260+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.07268v1","created_at":"2026-07-05T06:59:42.914260+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.07268","created_at":"2026-07-05T06:59:42.914260+00:00"},{"alias_kind":"pith_short_12","alias_value":"4WHFA4G2B2SK","created_at":"2026-07-05T06:59:42.914260+00:00"},{"alias_kind":"pith_short_16","alias_value":"4WHFA4G2B2SKVDNC","created_at":"2026-07-05T06:59:42.914260+00:00"},{"alias_kind":"pith_short_8","alias_value":"4WHFA4G2","created_at":"2026-07-05T06:59:42.914260+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/4WHFA4G2B2SKVDNCPJIN4JEA56","json":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56.json","graph_json":"https://pith.science/api/pith-number/4WHFA4G2B2SKVDNCPJIN4JEA56/graph.json","events_json":"https://pith.science/api/pith-number/4WHFA4G2B2SKVDNCPJIN4JEA56/events.json","paper":"https://pith.science/paper/4WHFA4G2"},"agent_actions":{"view_html":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56","download_json":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56.json","view_paper":"https://pith.science/paper/4WHFA4G2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.07268&json=true","fetch_graph":"https://pith.science/api/pith-number/4WHFA4G2B2SKVDNCPJIN4JEA56/graph.json","fetch_events":"https://pith.science/api/pith-number/4WHFA4G2B2SKVDNCPJIN4JEA56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56/action/storage_attestation","attest_author":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56/action/author_attestation","sign_citation":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56/action/citation_signature","submit_replication":"https://pith.science/pith/4WHFA4G2B2SKVDNCPJIN4JEA56/action/replication_record"}},"created_at":"2026-07-05T06:59:42.914260+00:00","updated_at":"2026-07-05T06:59:42.914260+00:00"}