{"id":"W4385498284","doi":"10.1016/j.future.2023.07.033","title":"Adaptive differential privacy in vertical federated learning for mobility forecasting","year":2023,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Differential privacy; Initialization; Federated learning; Feature (linguistics); Convergence (economics); Adaptation (eye); Information privacy; Artificial intelligence; Weighting; Machine learning; Computer security; Data mining","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":[0.005049174,0.0006348414,0.001854023,0.0008738076,0.00107104,0.002131471,0.002521779,0.001633006,0.00178058],"category_scores_gemma":[0.01594515,0.0005447956,0.0008250378,0.001588136,0.001515627,0.00500032,0.003599955,0.00222058,0.0003627197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820783,"about_ca_system_score_gemma":0.002169828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004676973,"about_ca_topic_score_gemma":0.003824854,"domain_scores_codex":[0.9976171,0.0007537567,0.0001406401,0.0005563435,0.000426326,0.0005057555],"domain_scores_gemma":[0.9924052,0.004855013,0.0004778596,0.001381906,0.000635072,0.0002450118],"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":[0.000703415,0.0002748662,0.004496522,0.0000736919,0.00009497053,0.0001420472,0.0001778771,0.8178841,0.001369231,0.04912756,0.002497878,0.1231579],"study_design_scores_gemma":[0.00000760757,0.00002212098,0.0001396746,0.00000452238,0.000006124548,0.00001598713,0.00001535517,0.9780196,0.0003610423,0.02125711,0.0001464819,0.000004301816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07058751,0.0003682188,0.9265943,0.0006487277,0.00006847591,0.00003912655,0.0002532796,0.0004453878,0.000995016],"genre_scores_gemma":[0.9620846,0.0001403847,0.03562095,0.0001287103,0.00005580988,0.00003830373,0.0002452705,0.00002828186,0.001657604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005049174,"threshold_uncertainty_score":0.02670288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07381806007811581,"score_gpt":0.276820564383731,"score_spread":0.2030025043056152,"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."}}