{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004082085,0.001022434,0.001550155,0.0004976089,0.0007009322,0.001395754,0.002725889,0.001740149,0.001367979],"category_scores_gemma":[0.008430873,0.0006129249,0.0008822574,0.001139694,0.001887366,0.003982679,0.003378036,0.002490418,0.000400971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009298561,"about_ca_system_score_gemma":0.001213604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006127628,"about_ca_topic_score_gemma":0.0005627694,"domain_scores_codex":[0.9974596,0.0009623623,0.0001214042,0.0006005905,0.0006971326,0.0001589706],"domain_scores_gemma":[0.9952678,0.002106477,0.0005551491,0.001512838,0.0004290129,0.0001287338],"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":[0.0001643869,0.00009247258,0.0005491104,0.00008933411,0.00009307172,0.0001810508,0.0001335624,0.8488886,0.004611204,0.07821263,0.001234885,0.06574964],"study_design_scores_gemma":[0.00001610064,0.00003438202,0.00003826564,0.00000384134,0.00000732472,0.00004160903,0.000009379535,0.9655957,0.001098676,0.03265425,0.0004945214,0.000005929029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003140463,0.000036401,0.9963111,0.0001188903,0.000009816661,0.00001360528,0.000013896,0.00005509318,0.0003006975],"genre_scores_gemma":[0.5671107,0.0002227828,0.428504,0.0002805927,0.0001465853,0.000215118,0.0001933638,0.0001071182,0.003219771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004082085,"threshold_uncertainty_score":0.02158839,"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."}}