{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZOTUQZCTYSKHIXFMI5DGL76VXA","short_pith_number":"pith:ZOTUQZCT","canonical_record":{"source":{"id":"2412.08460","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T15:25:38Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"0d328d06b09a75fdc6795be307491d34e332464724b3d554a1183c323fa4f4e6","abstract_canon_sha256":"5a085a213f63c7c83232d6589fd5f8d19898602559240c8790a5bb5f351927f2"},"schema_version":"1.0"},"canonical_sha256":"cba7486453c494745cac474665ffd5b804f40277e43c96b9da0c280e8a0eed21","source":{"kind":"arxiv","id":"2412.08460","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08460","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08460v2","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08460","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_12","alias_value":"ZOTUQZCTYSKH","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_16","alias_value":"ZOTUQZCTYSKHIXFM","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_8","alias_value":"ZOTUQZCT","created_at":"2026-07-05T10:35:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZOTUQZCTYSKHIXFMI5DGL76VXA","target":"record","payload":{"canonical_record":{"source":{"id":"2412.08460","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T15:25:38Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"0d328d06b09a75fdc6795be307491d34e332464724b3d554a1183c323fa4f4e6","abstract_canon_sha256":"5a085a213f63c7c83232d6589fd5f8d19898602559240c8790a5bb5f351927f2"},"schema_version":"1.0"},"canonical_sha256":"cba7486453c494745cac474665ffd5b804f40277e43c96b9da0c280e8a0eed21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:35:43.289780Z","signature_b64":"sD9pTESJT+totMGphmPlamMUeTpa0ycsH9+4q1tDNOw1y+q3TkA34HqJnDtcS0mL3q8vbNkW1lgJ7zDFWf/MAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cba7486453c494745cac474665ffd5b804f40277e43c96b9da0c280e8a0eed21","last_reissued_at":"2026-07-05T10:35:43.289155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:35:43.289155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.08460","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:35:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f2yHD5t0ev65gNITGNlt/VJ3PrNm7a4kaqbvrq5VKRVdT6rog2vu4TLoEMh1hIyDA6Th01ODRy3CaCwVPFAgCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:48:14.402328Z"},"content_sha256":"aced7b8b6daf3452c9d9dcf96320e7cf634a3a78e7c3439d65577e719fc850ba","schema_version":"1.0","event_id":"sha256:aced7b8b6daf3452c9d9dcf96320e7cf634a3a78e7c3439d65577e719fc850ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZOTUQZCTYSKHIXFMI5DGL76VXA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Fermin Orozco, Hongkai Wen, Johan Wahlstr\\\"om, Man Luo, Pedro Porto Buarque de Gusm\\~ao","submitted_at":"2024-12-11T15:25:38Z","abstract_excerpt":"Deep-learning based traffic prediction models require vast amounts of data to learn embedded spatial and temporal dependencies. The inherent privacy and commercial sensitivity of such data has encouraged a shift towards decentralised data-driven methods, such as Federated Learning (FL). Under a traditional Machine Learning paradigm, traffic flow prediction models can capture spatial and temporal relationships within centralised data. In reality, traffic data is likely distributed across separate data silos owned by multiple stakeholders. In this work, a cross-silo FL setting is motivated to fa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08460","kind":"arxiv","version":2},"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/2412.08460/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":"2026-07-05T10:35:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a3n3vyURBPa0cm74V/JZNq/kddXTwm8tOlRG7/yW30lySyLuQh50lfeJqAHJvKObNjQORd/oMwe1rP80NJPNDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:48:14.402902Z"},"content_sha256":"b9269c0209cde3e4835b220af8c96b8f477bb121c73fe8d556ea16e50ce918e0","schema_version":"1.0","event_id":"sha256:b9269c0209cde3e4835b220af8c96b8f477bb121c73fe8d556ea16e50ce918e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/bundle.json","state_url":"https://pith.science/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/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":"2026-08-08T07:48:14Z","links":{"resolver":"https://pith.science/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA","bundle":"https://pith.science/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/bundle.json","state":"https://pith.science/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZOTUQZCTYSKHIXFMI5DGL76VXA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZOTUQZCTYSKHIXFMI5DGL76VXA","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":"5a085a213f63c7c83232d6589fd5f8d19898602559240c8790a5bb5f351927f2","cross_cats_sorted":["cs.AI","cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T15:25:38Z","title_canon_sha256":"0d328d06b09a75fdc6795be307491d34e332464724b3d554a1183c323fa4f4e6"},"schema_version":"1.0","source":{"id":"2412.08460","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08460","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08460v2","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08460","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_12","alias_value":"ZOTUQZCTYSKH","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_16","alias_value":"ZOTUQZCTYSKHIXFM","created_at":"2026-07-05T10:35:43Z"},{"alias_kind":"pith_short_8","alias_value":"ZOTUQZCT","created_at":"2026-07-05T10:35:43Z"}],"graph_snapshots":[{"event_id":"sha256:b9269c0209cde3e4835b220af8c96b8f477bb121c73fe8d556ea16e50ce918e0","target":"graph","created_at":"2026-07-05T10:35:43Z","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/2412.08460/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep-learning based traffic prediction models require vast amounts of data to learn embedded spatial and temporal dependencies. The inherent privacy and commercial sensitivity of such data has encouraged a shift towards decentralised data-driven methods, such as Federated Learning (FL). Under a traditional Machine Learning paradigm, traffic flow prediction models can capture spatial and temporal relationships within centralised data. In reality, traffic data is likely distributed across separate data silos owned by multiple stakeholders. In this work, a cross-silo FL setting is motivated to fa","authors_text":"Fermin Orozco, Hongkai Wen, Johan Wahlstr\\\"om, Man Luo, Pedro Porto Buarque de Gusm\\~ao","cross_cats":["cs.AI","cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T15:25:38Z","title":"Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08460","kind":"arxiv","version":2},"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:aced7b8b6daf3452c9d9dcf96320e7cf634a3a78e7c3439d65577e719fc850ba","target":"record","created_at":"2026-07-05T10:35:43Z","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":"5a085a213f63c7c83232d6589fd5f8d19898602559240c8790a5bb5f351927f2","cross_cats_sorted":["cs.AI","cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T15:25:38Z","title_canon_sha256":"0d328d06b09a75fdc6795be307491d34e332464724b3d554a1183c323fa4f4e6"},"schema_version":"1.0","source":{"id":"2412.08460","kind":"arxiv","version":2}},"canonical_sha256":"cba7486453c494745cac474665ffd5b804f40277e43c96b9da0c280e8a0eed21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cba7486453c494745cac474665ffd5b804f40277e43c96b9da0c280e8a0eed21","first_computed_at":"2026-07-05T10:35:43.289155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:35:43.289155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sD9pTESJT+totMGphmPlamMUeTpa0ycsH9+4q1tDNOw1y+q3TkA34HqJnDtcS0mL3q8vbNkW1lgJ7zDFWf/MAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:35:43.289780Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.08460","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aced7b8b6daf3452c9d9dcf96320e7cf634a3a78e7c3439d65577e719fc850ba","sha256:b9269c0209cde3e4835b220af8c96b8f477bb121c73fe8d556ea16e50ce918e0"],"state_sha256":"a9b7ea6c08a042b848e7f64caba3e075e95b016565c608adae83864f78e2a86e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OWEW8bv1yMQt3W5n1eTL7S+dn6qLPEoYfZF4iNe1XY05N/SozqmC2mwfBydsX+2cpB1378HdS+INOK5whiy+Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T07:48:14.407597Z","bundle_sha256":"a87e2f7efb65fcd4881087ac0298ecce8542aadb5b5badea39b4aa5eeb15471a"}}