{"id":"W4385801345","doi":"10.1109/vtc2023-spring57618.2023.10199618","title":"Minimizing Energy Consumption for Decentralized Federated Learning Using D2D Communications","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Toronto","funders":"","keywords":"Computer science; Energy consumption; Scheduling (production processes); Distributed computing; News aggregator; Exploit; Overhead (engineering); Computer network; Mathematical optimization","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":[0.001555446,0.0007069565,0.001152615,0.0004368186,0.0008977661,0.001190497,0.001780369,0.001147766,0.001313126],"category_scores_gemma":[0.003448951,0.0003542538,0.0005125058,0.0009807205,0.0006659095,0.002010138,0.001908799,0.0008982402,0.0002333044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009732308,"about_ca_system_score_gemma":0.001592075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002478201,"about_ca_topic_score_gemma":0.003039905,"domain_scores_codex":[0.9988557,0.0003682532,0.00005665271,0.0003120157,0.0002391149,0.0001682571],"domain_scores_gemma":[0.9983748,0.0008005253,0.0001800423,0.000372582,0.00018474,0.00008730946],"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":[0.0002913373,0.000165247,0.001068591,0.0001176486,0.00004512025,0.0001608532,0.00008501109,0.857264,0.003608047,0.01359846,0.00261931,0.1209764],"study_design_scores_gemma":[0.00001082993,0.00003318601,0.00009864521,0.000003563887,0.000005490404,0.00003782642,0.00001713518,0.9933599,0.000858736,0.00516791,0.0004024789,0.000004373597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02257085,0.0002405456,0.9748199,0.0002964594,0.00003271221,0.00004863046,0.00006159719,0.0004654498,0.001463807],"genre_scores_gemma":[0.8999891,0.0001913606,0.09756947,0.000176442,0.00002966473,0.0001162101,0.0001135098,0.00003285259,0.001781374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002478201,"threshold_uncertainty_score":0.008226037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486674268992632,"score_gpt":0.3561989927980492,"score_spread":0.207531565898786,"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."}}