{"id":"W4297911727","doi":"10.1109/iccc55456.2022.9880740","title":"Leveraging Fully-decoupled Radio Access Network for Wireless Federated Learning Acceleration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CIC International Conference on Communications in China (ICCC)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Telecommunications link; Computer science; Computer network; Radio access network; Base station; Leverage (statistics); Wireless; Radio resource management; Wireless network; Cloud computing; Distributed computing; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001715781,0.0003032267,0.0003235098,0.0005309553,0.001839195,0.001047467,0.04252521,0.0001127824,0.0003183409],"category_scores_gemma":[0.002116094,0.0003621955,0.0001045171,0.001164877,0.0001281472,0.001219524,0.04115386,0.001663001,0.00001479838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008643559,"about_ca_system_score_gemma":0.0003349002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001900465,"about_ca_topic_score_gemma":0.0002280748,"domain_scores_codex":[0.9966722,0.0005973971,0.0007049359,0.0007999346,0.0007246566,0.0005009144],"domain_scores_gemma":[0.9929826,0.0007190854,0.0004783268,0.005498074,0.0002611708,0.00006069967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003111279,0.001222675,0.006090026,0.00005390068,0.0003322895,0.00002915938,0.001952512,0.07634242,0.002801079,0.65418,0.1257622,0.1309227],"study_design_scores_gemma":[0.0007295886,0.000108021,0.0009547742,0.00006976524,0.000006133962,0.00001574403,0.0002631652,0.9119823,0.0005354595,0.0770219,0.00795757,0.0003555942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08620504,0.0004986478,0.7222956,0.1607539,0.004554693,0.002431056,0.0001935888,0.001848667,0.02121883],"genre_scores_gemma":[0.9600716,0.0004089397,0.03675525,0.0004595942,0.00009962326,0.0009449065,0.0008646907,0.00003441847,0.0003610023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8738666,"threshold_uncertainty_score":0.9999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1259738548702108,"score_gpt":0.3635430158098524,"score_spread":0.2375691609396417,"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."}}