{"id":"W3205260830","doi":"10.1109/jsac.2021.3118352","title":"Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Waterloo","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; Distributed computing; Distributed learning; Artificial intelligence; World Wide Web","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":[0.001944804,0.001078049,0.00153848,0.0005028201,0.0005944578,0.001126692,0.001801186,0.001656876,0.001001257],"category_scores_gemma":[0.003126522,0.0004826256,0.000524858,0.0004745072,0.001277277,0.00146364,0.001461138,0.001197412,0.0001407809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123798,"about_ca_system_score_gemma":0.001408199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00438971,"about_ca_topic_score_gemma":0.004371006,"domain_scores_codex":[0.9991995,0.0002758051,0.00003556854,0.0001987139,0.0001448338,0.0001455456],"domain_scores_gemma":[0.9982096,0.0009358432,0.0002879786,0.0001647858,0.0002657691,0.0001360923],"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":[0.00003183363,0.00003920394,0.0002593839,0.00001582344,0.00002332686,0.00004178657,0.00001608035,0.9889323,0.0003809,0.002894609,0.0001974352,0.007167491],"study_design_scores_gemma":[0.000005829633,0.000009793646,0.00002437623,0.000001426176,0.00000274432,0.000003364675,0.000004158296,0.9980094,0.0001128895,0.001753771,0.00007070138,0.000001516059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03063467,0.0001891696,0.9666617,0.0003760255,0.00004029329,0.00005277797,0.00002455064,0.0002681043,0.001752754],"genre_scores_gemma":[0.9141209,0.0001206409,0.0836273,0.0001690166,0.00004247842,0.0000910562,0.00003735991,0.00003658437,0.001754798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00438971,"threshold_uncertainty_score":0.01028526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191208882038356,"score_gpt":0.3230193121981485,"score_spread":0.203898423994313,"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."}}