{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OWRFZFOB3AALZNHNBL2JWWFMBM","short_pith_number":"pith:OWRFZFOB","canonical_record":{"source":{"id":"2305.11654","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-19T13:09:33Z","cross_cats_sorted":[],"title_canon_sha256":"189079442bca59deaec957b92c078d4a006448b0b414bcfa9b077e6c6c405da1","abstract_canon_sha256":"c66bcb4bd4496f93762ad27c21d9da9becdfaab8d189ee34e07ca16915a44dc1"},"schema_version":"1.0"},"canonical_sha256":"75a25c95c1d800bcb4ed0af49b58ac0b3d1731f6c8e1c2dc58726cbf298be712","source":{"kind":"arxiv","id":"2305.11654","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.11654","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"arxiv_version","alias_value":"2305.11654v1","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11654","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_12","alias_value":"OWRFZFOB3AAL","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_16","alias_value":"OWRFZFOB3AALZNHN","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_8","alias_value":"OWRFZFOB","created_at":"2026-07-05T06:11:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OWRFZFOB3AALZNHNBL2JWWFMBM","target":"record","payload":{"canonical_record":{"source":{"id":"2305.11654","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-19T13:09:33Z","cross_cats_sorted":[],"title_canon_sha256":"189079442bca59deaec957b92c078d4a006448b0b414bcfa9b077e6c6c405da1","abstract_canon_sha256":"c66bcb4bd4496f93762ad27c21d9da9becdfaab8d189ee34e07ca16915a44dc1"},"schema_version":"1.0"},"canonical_sha256":"75a25c95c1d800bcb4ed0af49b58ac0b3d1731f6c8e1c2dc58726cbf298be712","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:11:49.716516Z","signature_b64":"w6biFm+5QgR0wfASbn1g1qen+47WJp7Lvef0evQnRkKNmsx/GWS6f3B9ZLC0KkvkYcqakIrYYbPwrUCpsuxbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75a25c95c1d800bcb4ed0af49b58ac0b3d1731f6c8e1c2dc58726cbf298be712","last_reissued_at":"2026-07-05T06:11:49.716065Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:11:49.716065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.11654","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:11:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SphOS4j/gpWdB4bTzpf2QEONFiLllytjluXr7U7g0ViFnIt5eRibqbhhcAh8xxEbRlVkkCORqOREi9QY3sVDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T20:09:31.938740Z"},"content_sha256":"da96fa4e357e76fa8fe3287e418863f77080e2c81783787198221ae8ee57aa51","schema_version":"1.0","event_id":"sha256:da96fa4e357e76fa8fe3287e418863f77080e2c81783787198221ae8ee57aa51"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OWRFZFOB3AALZNHNBL2JWWFMBM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alois Knoll, Andreas Festag, Lingjuan Lyu, Rui Song, Wei Jiang","submitted_at":"2023-05-19T13:09:33Z","abstract_excerpt":"Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11654","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/2305.11654/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:11:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fzprwixo/aZ8HrNQdRFKrCRFqzKNJ9+PlV8bIHgCuURGMweZuNRZVVcNzMTcKQg97TNzadh30wtu0HvYOFQNBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T20:09:31.939304Z"},"content_sha256":"9afc92a732835d42dba994747cda65c131067cfc017f52aa8577bd71e321f5d3","schema_version":"1.0","event_id":"sha256:9afc92a732835d42dba994747cda65c131067cfc017f52aa8577bd71e321f5d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/bundle.json","state_url":"https://pith.science/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T20:09:31Z","links":{"resolver":"https://pith.science/pith/OWRFZFOB3AALZNHNBL2JWWFMBM","bundle":"https://pith.science/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/bundle.json","state":"https://pith.science/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OWRFZFOB3AALZNHNBL2JWWFMBM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OWRFZFOB3AALZNHNBL2JWWFMBM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c66bcb4bd4496f93762ad27c21d9da9becdfaab8d189ee34e07ca16915a44dc1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-19T13:09:33Z","title_canon_sha256":"189079442bca59deaec957b92c078d4a006448b0b414bcfa9b077e6c6c405da1"},"schema_version":"1.0","source":{"id":"2305.11654","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.11654","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"arxiv_version","alias_value":"2305.11654v1","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11654","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_12","alias_value":"OWRFZFOB3AAL","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_16","alias_value":"OWRFZFOB3AALZNHN","created_at":"2026-07-05T06:11:49Z"},{"alias_kind":"pith_short_8","alias_value":"OWRFZFOB","created_at":"2026-07-05T06:11:49Z"}],"graph_snapshots":[{"event_id":"sha256:9afc92a732835d42dba994747cda65c131067cfc017f52aa8577bd71e321f5d3","target":"graph","created_at":"2026-07-05T06:11:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.11654/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes","authors_text":"Alois Knoll, Andreas Festag, Lingjuan Lyu, Rui Song, Wei Jiang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-19T13:09:33Z","title":"V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11654","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:da96fa4e357e76fa8fe3287e418863f77080e2c81783787198221ae8ee57aa51","target":"record","created_at":"2026-07-05T06:11:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c66bcb4bd4496f93762ad27c21d9da9becdfaab8d189ee34e07ca16915a44dc1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-19T13:09:33Z","title_canon_sha256":"189079442bca59deaec957b92c078d4a006448b0b414bcfa9b077e6c6c405da1"},"schema_version":"1.0","source":{"id":"2305.11654","kind":"arxiv","version":1}},"canonical_sha256":"75a25c95c1d800bcb4ed0af49b58ac0b3d1731f6c8e1c2dc58726cbf298be712","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75a25c95c1d800bcb4ed0af49b58ac0b3d1731f6c8e1c2dc58726cbf298be712","first_computed_at":"2026-07-05T06:11:49.716065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:11:49.716065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w6biFm+5QgR0wfASbn1g1qen+47WJp7Lvef0evQnRkKNmsx/GWS6f3B9ZLC0KkvkYcqakIrYYbPwrUCpsuxbDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:11:49.716516Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.11654","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da96fa4e357e76fa8fe3287e418863f77080e2c81783787198221ae8ee57aa51","sha256:9afc92a732835d42dba994747cda65c131067cfc017f52aa8577bd71e321f5d3"],"state_sha256":"c720c136ad16b5e0b8c03a8af438ec02a441e5a380fe720df429ed883bb914b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h78ThwP+OcyYErlS1mydiu66qIoeKgOGX00sTegAlkg4Ip4RHyEkfAfka4ZgFFMJrRoiQ3SW1DgziU/3DXbRCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T20:09:31.943889Z","bundle_sha256":"e477bfa7fe7f1e8fde203deb2da708f5ce6b2b86a01cb0287a635de5f539a86a"}}