{"id":"W4319778953","doi":"10.1109/jsac.2023.3242704","title":"Split Learning Over Wireless Networks: Parallel Design and Resource Management","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies; National Natural Science Foundation of China; Peng Cheng Laboratory","keywords":"Computer science; Latency (audio); Cluster analysis; Artificial intelligence; Partition (number theory); Wireless; Machine learning; Computer network; Operating system; Telecommunications","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":[0.001732503,0.00088691,0.000761122,0.0006190269,0.0008236816,0.001635071,0.002568155,0.0006398116,0.004356974],"category_scores_gemma":[0.003689691,0.0005263847,0.0005574795,0.0006722427,0.001134667,0.003475714,0.001875328,0.001434804,0.0008064697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001814712,"about_ca_system_score_gemma":0.002169826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004348125,"about_ca_topic_score_gemma":0.005595502,"domain_scores_codex":[0.9986268,0.0003476463,0.00008463603,0.000340793,0.0003477727,0.0002523618],"domain_scores_gemma":[0.998159,0.000608601,0.0001397325,0.0005295555,0.0004132269,0.000149938],"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":[0.0005256917,0.0003372427,0.001879854,0.000151145,0.00008345109,0.000140396,0.0002136635,0.6371456,0.008070654,0.04150706,0.006619682,0.3033255],"study_design_scores_gemma":[0.00001685712,0.00005678311,0.00006122884,0.000004258952,0.000008016347,0.00002240308,0.00001851318,0.9895326,0.001884813,0.00733883,0.001050956,0.000004817431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02174054,0.0002354053,0.9713687,0.0003305846,0.00006242126,0.000152278,0.00004328538,0.00161057,0.004456133],"genre_scores_gemma":[0.7276198,0.0003254258,0.2657636,0.0002351575,0.0000724175,0.0003915387,0.0001820072,0.000177548,0.00523254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004356974,"threshold_uncertainty_score":0.01457554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05177587151592061,"score_gpt":0.3009152761501048,"score_spread":0.2491394046341842,"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."}}