{"id":"W4379618880","doi":"10.1109/jiot.2023.3283855","title":"FedMint: Intelligent Bilateral Client Selection in Federated Learning With Newcomer IoT Devices","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Selection (genetic algorithm); Internet of Things; Computer network; Artificial intelligence; Multimedia; Human–computer interaction; World Wide Web","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001093028,0.0001975916,0.0002701121,0.0007553458,0.0001146664,0.0005112306,0.007431785,0.0001192229,0.00002774795],"category_scores_gemma":[0.001556466,0.000156937,0.00006626668,0.001178689,0.00007102371,0.0009036561,0.006367789,0.001161796,0.00005212582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002134895,"about_ca_system_score_gemma":0.00007832854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003459781,"about_ca_topic_score_gemma":0.00007917752,"domain_scores_codex":[0.9979765,0.0001338705,0.0005770702,0.0003777571,0.0004846103,0.0004501451],"domain_scores_gemma":[0.9985709,0.0001255316,0.0004449493,0.0006106737,0.0001673375,0.00008062948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007748632,0.0007509697,0.361126,0.0003487163,0.0009116593,0.001887494,0.01745127,0.03219032,0.06529407,0.0009835525,0.1839769,0.3343042],"study_design_scores_gemma":[0.0005790595,0.0006912493,0.006954679,0.0008516098,0.00001076832,0.0008822196,0.0002825867,0.8882204,0.09161754,0.00752355,0.002012106,0.0003742609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320665,0.00004136714,0.1628899,0.003758047,0.0005946155,0.00009017379,3.409414e-7,0.0004030279,0.0001561155],"genre_scores_gemma":[0.9639233,0.00004856218,0.03558454,0.0001333802,0.00004529687,0.000004753237,0.000001427323,0.00001835533,0.0002403372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.85603,"threshold_uncertainty_score":0.9979385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03323289694753705,"score_gpt":0.2863583501853905,"score_spread":0.2531254532378535,"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."}}