{"id":"W4295791159","doi":"10.1109/icc45855.2022.9882289","title":"Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Energy consumption; Scheme (mathematics); Enhanced Data Rates for GSM Evolution; Node (physics); Heuristic; Convergence (economics); Process (computing); Efficient energy use; Computation; Data mining; Similarity (geometry); Edge computing; Machine learning; Artificial intelligence; Distributed computing; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001394751,0.0005856982,0.0009609043,0.0004430242,0.0008627076,0.0007498015,0.002550552,0.00100289,0.001253202],"category_scores_gemma":[0.003586902,0.0002349886,0.0004787434,0.0007169064,0.0008024754,0.002767798,0.002705642,0.001136803,0.0002596861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006059848,"about_ca_system_score_gemma":0.0007203344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001151857,"about_ca_topic_score_gemma":0.001801259,"domain_scores_codex":[0.9990973,0.000263921,0.00006185075,0.000247876,0.0001806724,0.00014841],"domain_scores_gemma":[0.99843,0.0005206127,0.0001315703,0.0005216845,0.0002861587,0.0001101096],"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":[0.0008848977,0.0004000203,0.002779954,0.00008813486,0.00006851942,0.0001929216,0.0002968988,0.7063207,0.01205987,0.01745676,0.003064106,0.2563873],"study_design_scores_gemma":[0.00001178223,0.00004621293,0.0001309917,0.000003102029,0.000004478336,0.00003367665,0.00002691808,0.991178,0.002522053,0.00577708,0.0002598621,0.000005867425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06165151,0.0001159228,0.9364129,0.0001504927,0.00003296812,0.00005540517,0.00006825912,0.0005723125,0.0009402073],"genre_scores_gemma":[0.9000475,0.00005098758,0.09809831,0.0001404493,0.000018264,0.00008567239,0.0001990097,0.00003469869,0.001325105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002550552,"threshold_uncertainty_score":0.007376254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.234802844293978,"score_gpt":0.386814217489454,"score_spread":0.152011373195476,"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."}}