{"id":"W3123346250","doi":"10.1109/globecom42002.2020.9322159","title":"Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Blockchain; Federated learning; Single point of failure; Trustworthiness; Artificial intelligence; Scheme (mathematics); Information privacy; Machine learning; Safeguarding; Deep learning; Distributed computing; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0003182395,0.0001999974,0.0002427581,0.00005830165,0.0002871815,0.0002887115,0.01297605,0.0001838566,0.00004432227],"category_scores_gemma":[0.009519731,0.0001860519,0.0000881022,0.0007570289,0.00004845719,0.0002114949,0.02897091,0.0004150426,0.00003188798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876306,"about_ca_system_score_gemma":0.00005131982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001003312,"about_ca_topic_score_gemma":0.000003508345,"domain_scores_codex":[0.9982142,0.00006060393,0.0002898526,0.0006961834,0.0002177919,0.0005213056],"domain_scores_gemma":[0.9970599,0.0001907448,0.0001127682,0.002380013,0.0001114356,0.0001451128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004738123,0.00007119385,0.002413674,0.00005147294,0.0001726863,0.0001157735,0.0001389365,0.03936314,0.001561342,0.008656452,0.8306242,0.1167838],"study_design_scores_gemma":[0.0004018014,0.0001424993,0.00008523503,0.000008014606,0.000006940534,0.000005891356,0.0000261299,0.968995,0.0023195,0.005135669,0.02263363,0.0002396671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006273576,0.0001009732,0.9263099,0.06284893,0.0001680114,0.0003376799,0.000001741488,0.003217619,0.0007415842],"genre_scores_gemma":[0.7346923,0.00001502897,0.2626449,0.002337013,0.0001040857,0.00004595685,0.00003100515,0.00002272268,0.0001070095],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9296319,"threshold_uncertainty_score":0.9988235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03052048685015984,"score_gpt":0.2537272549038195,"score_spread":0.2232067680536597,"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."}}