{"id":"W3170139470","doi":"10.1109/comst.2021.3086014","title":"Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Science and Technology Commission of Shanghai Municipality; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Cloud computing; Overhead (engineering); Wireless network; Reinforcement learning; Distributed computing; Software deployment; Open research; Computer network; Artificial intelligence; Telecommunications; 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":[0.002163327,0.001001734,0.001010578,0.001183602,0.0004205444,0.001809389,0.001607633,0.001270324,0.002041243],"category_scores_gemma":[0.003999858,0.0004817755,0.0005960785,0.002863513,0.001015511,0.002526976,0.001513707,0.003887527,0.001084255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121734,"about_ca_system_score_gemma":0.0009401947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050327,"about_ca_topic_score_gemma":0.000964787,"domain_scores_codex":[0.9985274,0.0006014382,0.00009337525,0.0002004057,0.0004915172,0.00008595882],"domain_scores_gemma":[0.9981008,0.001236788,0.0001035033,0.0002670256,0.0002496703,0.00004229958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004466485,0.0001113161,0.001134922,0.001269159,0.00009978186,0.0001049401,0.000199301,0.1180969,0.002428022,0.1686211,0.01835775,0.6895322],"study_design_scores_gemma":[0.00001987684,0.00009901694,0.0005755156,0.0002794875,0.00003023656,0.0003317147,0.00008238359,0.7226529,0.0017165,0.2009051,0.07327154,0.00003571507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.00246261,0.05589542,0.9317446,0.002794864,0.0003724352,0.000094116,0.00008445672,0.0005108253,0.006040797],"genre_scores_gemma":[0.2482633,0.1734958,0.563164,0.001270337,0.003108112,0.0007679196,0.0005118805,0.0002543637,0.009164331],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002163327,"threshold_uncertainty_score":0.01144087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04175595437979043,"score_gpt":0.306744260300647,"score_spread":0.2649883059208565,"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."}}