{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DVDLGT2RZMDMQEE33F43532NRJ","short_pith_number":"pith:DVDLGT2R","schema_version":"1.0","canonical_sha256":"1d46b34f51cb06c8109bd979beef4d8a5c84802fe172176fbe1962011d7ed0d0","source":{"kind":"arxiv","id":"2408.03446","version":1},"attestation_state":"computed","paper":{"title":"Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.NI","authors_text":"Lin X. Cai, Lu Wang, Morteza Hashemi, Peiyuan Guan, Zhou Ni, Ziru Chen, Zongzhi Li","submitted_at":"2024-08-06T20:54:39Z","abstract_excerpt":"Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited availabl"},"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":"2408.03446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-08-06T20:54:39Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"9b1e41ea9c2563d2568fae487d157f33a1ad67bae1553a54b91e8e58cdee7413","abstract_canon_sha256":"57475dd91f97f72e214d3c93c50f28e43eecfcb2e843aadefb1d0d1262451098"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:58.690541Z","signature_b64":"qmHr0gi9+G5vM5tP85hXIv3ZBKwtg5te+g/vSUOXj/q3LQ2ok/H3fKJiBqQL0NF4opkvPAxsidEISfqfHW2/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d46b34f51cb06c8109bd979beef4d8a5c84802fe172176fbe1962011d7ed0d0","last_reissued_at":"2026-07-05T08:52:58.690070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:58.690070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.NI","authors_text":"Lin X. Cai, Lu Wang, Morteza Hashemi, Peiyuan Guan, Zhou Ni, Ziru Chen, Zongzhi Li","submitted_at":"2024-08-06T20:54:39Z","abstract_excerpt":"Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited availabl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03446","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/2408.03446/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":"2408.03446","created_at":"2026-07-05T08:52:58.690126+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03446v1","created_at":"2026-07-05T08:52:58.690126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03446","created_at":"2026-07-05T08:52:58.690126+00:00"},{"alias_kind":"pith_short_12","alias_value":"DVDLGT2RZMDM","created_at":"2026-07-05T08:52:58.690126+00:00"},{"alias_kind":"pith_short_16","alias_value":"DVDLGT2RZMDMQEE3","created_at":"2026-07-05T08:52:58.690126+00:00"},{"alias_kind":"pith_short_8","alias_value":"DVDLGT2R","created_at":"2026-07-05T08:52:58.690126+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.09822","citing_title":"pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ","json":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ.json","graph_json":"https://pith.science/api/pith-number/DVDLGT2RZMDMQEE33F43532NRJ/graph.json","events_json":"https://pith.science/api/pith-number/DVDLGT2RZMDMQEE33F43532NRJ/events.json","paper":"https://pith.science/paper/DVDLGT2R"},"agent_actions":{"view_html":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ","download_json":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ.json","view_paper":"https://pith.science/paper/DVDLGT2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03446&json=true","fetch_graph":"https://pith.science/api/pith-number/DVDLGT2RZMDMQEE33F43532NRJ/graph.json","fetch_events":"https://pith.science/api/pith-number/DVDLGT2RZMDMQEE33F43532NRJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ/action/storage_attestation","attest_author":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ/action/author_attestation","sign_citation":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ/action/citation_signature","submit_replication":"https://pith.science/pith/DVDLGT2RZMDMQEE33F43532NRJ/action/replication_record"}},"created_at":"2026-07-05T08:52:58.690126+00:00","updated_at":"2026-07-05T08:52:58.690126+00:00"}