{"id":"W2960318545","doi":"10.48550/arxiv.1907.07735","title":"Learning Privately over Distributed Features: An ADMM Sharing Approach","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Federated learning; Raw data; Minification; Convergence (economics); Regular polygon; Convex optimization; Distributed learning; Differential privacy; Process (computing); Data mining; Distributed computing; Mathematics","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":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005611341,0.0005822182,0.0005545393,0.0004341252,0.0002842393,0.0005576217,0.05563062,0.0008859098,0.00002115474],"category_scores_gemma":[0.002829869,0.000675154,0.0002205289,0.00108499,0.0001726623,0.001472748,0.2466386,0.002667378,0.00006967999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004930815,"about_ca_system_score_gemma":0.0001949162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001932976,"about_ca_topic_score_gemma":0.00001218614,"domain_scores_codex":[0.9956411,0.0001964769,0.0002931034,0.002853558,0.000235149,0.0007805931],"domain_scores_gemma":[0.9830248,0.0001270453,0.0004617789,0.01603545,0.0001456399,0.0002052269],"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":[0.000124981,0.0006594728,0.07317463,0.0007196111,0.0005757812,0.0008472527,0.0004867945,0.7438138,0.0002106491,0.1471333,0.02973428,0.002519475],"study_design_scores_gemma":[0.0004259278,0.00008168229,0.007049484,0.0001238842,0.00004319416,0.00001092,0.00008886964,0.881097,0.0001587986,0.109284,0.0008460257,0.0007901705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2888893,0.00006451933,0.7057884,0.0002563764,0.0005074699,0.0004390769,0.00009255438,0.002321113,0.001641188],"genre_scores_gemma":[0.9650615,0.0001217006,0.03340873,0.00004136031,0.00006355148,0.000002368561,0.0006157341,0.00004465913,0.0006404009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6761722,"threshold_uncertainty_score":0.9996335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08326758260939596,"score_gpt":0.21682076193501,"score_spread":0.1335531793256141,"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."}}