{"id":"W4388040637","doi":"10.1109/pimrc56721.2023.10293927","title":"Probabilistic Client Sampling and Power Allocation for Wireless Federated Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Convergence (economics); Resource allocation; Wireless; Optimization problem; Lyapunov optimization; Sampling (signal processing); Convex optimization; Mathematical optimization; Machine learning; Artificial intelligence; Algorithm; Regular polygon; Computer network; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002688292,0.0006900606,0.001065259,0.0003822332,0.0004858183,0.0009534881,0.001685975,0.0009844323,0.001112589],"category_scores_gemma":[0.007802772,0.0003561465,0.0004137363,0.0006926513,0.001050342,0.001831806,0.00155614,0.00131755,0.0002705289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009632939,"about_ca_system_score_gemma":0.001417447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574688,"about_ca_topic_score_gemma":0.001604978,"domain_scores_codex":[0.9987572,0.0004950951,0.00006703483,0.0002709717,0.0002567459,0.0001530205],"domain_scores_gemma":[0.9971557,0.001709909,0.0002344528,0.0004013242,0.0003708229,0.0001278645],"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":[0.0001913863,0.0001233215,0.001080309,0.00004827471,0.00003361326,0.00006615048,0.00006558895,0.8945408,0.002075045,0.01215429,0.0009642754,0.0886569],"study_design_scores_gemma":[0.000004126743,0.00001404127,0.00004357028,0.000001675265,0.000001896471,0.00001050194,0.000004406586,0.9961618,0.0004806814,0.003194929,0.00008051975,0.00000178568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01562588,0.00009232345,0.9832817,0.0001320346,0.0000148484,0.00002814426,0.00001695678,0.000299744,0.0005084124],"genre_scores_gemma":[0.8817381,0.0001063198,0.1159243,0.0001623693,0.00003380097,0.0001297437,0.00006977522,0.00005862571,0.001777057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002688292,"threshold_uncertainty_score":0.0142172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05391063909348004,"score_gpt":0.3046666734187677,"score_spread":0.2507560343252876,"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."}}