{"id":"W4390102611","doi":"10.1016/j.dsp.2023.104353","title":"Killing two birds with one stone: Quantization achieves privacy in distributed learning","year":2023,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Computer science; Differential privacy; Distributed learning; Quantization (signal processing); Stochastic gradient descent; Federated learning; Artificial intelligence; Distributed computing; Theoretical computer science; Algorithm","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":[0.005890524,0.0005299607,0.001739575,0.0005869016,0.001889512,0.003544295,0.002724708,0.002496995,0.002482512],"category_scores_gemma":[0.02658337,0.0006083525,0.0007708236,0.001268345,0.005609512,0.008540268,0.006269653,0.004716339,0.0005145915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563675,"about_ca_system_score_gemma":0.001983011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362726,"about_ca_topic_score_gemma":0.001264538,"domain_scores_codex":[0.9949355,0.002033456,0.0002579426,0.0009702854,0.001248236,0.0005546385],"domain_scores_gemma":[0.9754572,0.01534135,0.0009727681,0.006720987,0.001017998,0.0004898278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008516873,0.000167259,0.00160057,0.000157199,0.0000887107,0.0001856778,0.0007227173,0.2455912,0.004540563,0.6278437,0.004859975,0.1133906],"study_design_scores_gemma":[0.00005047577,0.00005087568,0.0001335641,0.00001300655,0.00001354458,0.00005490501,0.00006980299,0.466358,0.001695973,0.5305746,0.0009715005,0.00001377322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03354562,0.0003069425,0.9609315,0.002128639,0.00007210137,0.00004224843,0.00006628785,0.0002249553,0.002681766],"genre_scores_gemma":[0.888412,0.0003644198,0.1061462,0.0005478545,0.0001844404,0.0001067669,0.0001016702,0.00009480166,0.004041813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005890524,"threshold_uncertainty_score":0.03115243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03206867446080338,"score_gpt":0.2791864865554489,"score_spread":0.2471178120946455,"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."}}