{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.004234899,0.0002593648,0.0004035593,0.0001446604,0.001145012,0.0003977056,0.02324526,0.0002503247,0.000003205257],"category_scores_gemma":[0.009617176,0.0002853204,0.000085336,0.001078209,0.0004415845,0.0003178016,0.03512498,0.0006985288,0.000004003009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295641,"about_ca_system_score_gemma":0.0001623329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002123158,"about_ca_topic_score_gemma":0.0004513348,"domain_scores_codex":[0.9955779,0.002575568,0.000635837,0.0005999477,0.0002225268,0.0003882283],"domain_scores_gemma":[0.9743403,0.004140336,0.0003788163,0.02048756,0.0005565254,0.0000964869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002274494,0.0008586292,0.004162868,0.0001484718,0.0003481468,0.000004150474,0.0002622528,0.0009244729,0.009715011,0.1131563,0.04800635,0.8223907],"study_design_scores_gemma":[0.0008422771,0.00007942016,0.001642172,0.0001584917,0.00006986038,0.00006054678,0.00005419257,0.3228372,0.01291761,0.1886788,0.4717319,0.0009274641],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000313393,0.005619811,0.9795908,0.01140629,0.0002279362,0.0009327015,0.0002817969,0.001424943,0.0002023127],"genre_scores_gemma":[0.39887,0.006344416,0.5894776,0.000120738,0.0001779701,0.001952263,0.002945072,0.00004747229,0.00006447705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8214632,"threshold_uncertainty_score":0.9999599,"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."}}