{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:REFYUQVQNZZNO6B2LOIGM4SWEC","short_pith_number":"pith:REFYUQVQ","schema_version":"1.0","canonical_sha256":"890b8a42b06e72d7783a5b9066725620930329114e4a354bebf55b63c7de9617","source":{"kind":"arxiv","id":"2208.05558","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Choong Seon Hong, Ehzaz Mustafa, Faisal Rehman, Junaid Shuja, Kashif Bilal, Latif U. Khan, Zhu Han","submitted_at":"2022-08-10T20:52:20Z","abstract_excerpt":"Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a significant need to deploy a digital twin-based intelligent transportation system. Although the twin-based implementation of vehicular networks can offer performance optimization. Modeling twins is a significantly challenging task. Machine learning (ML) can be a preferable solution to model such a virtual model, and "},"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":"2208.05558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-08-10T20:52:20Z","cross_cats_sorted":[],"title_canon_sha256":"e87f70c9ac9b06864228a16dfdb86f35f354bd3b82fa39110ef7fbf3ac97998a","abstract_canon_sha256":"d655dbada62f69ec2ae165152efdcc45adca91caeecea960d8fa798308bc49b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:47:43.963463Z","signature_b64":"UGcJvURBjIDT9TZvh7qMw+Q9qa+02lZpVncizALladm3yXw4tpMjs0MlpNI+lQ9+ROtoHyMNTR07gRhmc/1xAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"890b8a42b06e72d7783a5b9066725620930329114e4a354bebf55b63c7de9617","last_reissued_at":"2026-07-05T04:47:43.963068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:47:43.963068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Choong Seon Hong, Ehzaz Mustafa, Faisal Rehman, Junaid Shuja, Kashif Bilal, Latif U. Khan, Zhu Han","submitted_at":"2022-08-10T20:52:20Z","abstract_excerpt":"Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a significant need to deploy a digital twin-based intelligent transportation system. Although the twin-based implementation of vehicular networks can offer performance optimization. Modeling twins is a significantly challenging task. Machine learning (ML) can be a preferable solution to model such a virtual model, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05558","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/2208.05558/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":"2208.05558","created_at":"2026-07-05T04:47:43.963136+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.05558v1","created_at":"2026-07-05T04:47:43.963136+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05558","created_at":"2026-07-05T04:47:43.963136+00:00"},{"alias_kind":"pith_short_12","alias_value":"REFYUQVQNZZN","created_at":"2026-07-05T04:47:43.963136+00:00"},{"alias_kind":"pith_short_16","alias_value":"REFYUQVQNZZNO6B2","created_at":"2026-07-05T04:47:43.963136+00:00"},{"alias_kind":"pith_short_8","alias_value":"REFYUQVQ","created_at":"2026-07-05T04:47:43.963136+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/REFYUQVQNZZNO6B2LOIGM4SWEC","json":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC.json","graph_json":"https://pith.science/api/pith-number/REFYUQVQNZZNO6B2LOIGM4SWEC/graph.json","events_json":"https://pith.science/api/pith-number/REFYUQVQNZZNO6B2LOIGM4SWEC/events.json","paper":"https://pith.science/paper/REFYUQVQ"},"agent_actions":{"view_html":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC","download_json":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC.json","view_paper":"https://pith.science/paper/REFYUQVQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.05558&json=true","fetch_graph":"https://pith.science/api/pith-number/REFYUQVQNZZNO6B2LOIGM4SWEC/graph.json","fetch_events":"https://pith.science/api/pith-number/REFYUQVQNZZNO6B2LOIGM4SWEC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC/action/storage_attestation","attest_author":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC/action/author_attestation","sign_citation":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC/action/citation_signature","submit_replication":"https://pith.science/pith/REFYUQVQNZZNO6B2LOIGM4SWEC/action/replication_record"}},"created_at":"2026-07-05T04:47:43.963136+00:00","updated_at":"2026-07-05T04:47:43.963136+00:00"}