{"id":"W4226524907","doi":"10.1109/tits.2022.3157056","title":"FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"State Key Laboratory of Industrial Control Technology; Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Intelligent transportation system; Graph; Enhanced Data Rates for GSM Evolution; Data mining; Deep learning; Traffic flow (computer networking); Edge computing; Smart city; Traffic congestion; Distributed computing; Artificial intelligence; Computer network; Theoretical computer science; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006877358,0.0008481547,0.0008587361,0.0006995158,0.0004728166,0.0005966207,0.001804815,0.0008581624,0.001213868],"category_scores_gemma":[0.001759353,0.0002961524,0.0007152839,0.0008139865,0.0004614446,0.00149941,0.001162765,0.001070216,0.0002869725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009671223,"about_ca_system_score_gemma":0.001229523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01351926,"about_ca_topic_score_gemma":0.01567743,"domain_scores_codex":[0.9997122,0.00004995507,0.00001679005,0.00009500008,0.0000679041,0.00005809332],"domain_scores_gemma":[0.9995363,0.0001477748,0.00004881963,0.00009642634,0.0001320696,0.0000386652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001451183,0.0001883838,0.002248707,0.00004595682,0.00006930397,0.0001097825,0.00004885336,0.8101993,0.002082121,0.004532751,0.004462122,0.1758675],"study_design_scores_gemma":[0.000002699073,0.000009280713,0.0000685172,0.000001248588,0.000002619513,0.00000699709,0.000003402962,0.9979393,0.0002675158,0.001570775,0.0001258293,0.000001777346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05594102,0.0003195144,0.9383099,0.0002538956,0.00009775579,0.00006040696,0.0003862281,0.003219057,0.001412178],"genre_scores_gemma":[0.8161986,0.0002303523,0.1778916,0.0002872304,0.00004519701,0.0001364022,0.001798849,0.0001165725,0.003295138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01351926,"threshold_uncertainty_score":0.02688116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878456535698501,"score_gpt":0.2345109424485296,"score_spread":0.2157263770915446,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}