{"id":"W4414348218","doi":"10.1109/tai.2025.3609733","title":"Towards Vox Populi in Federated Learning: A Fair and Inclusive Client Selection Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Selection (genetic algorithm); Government (linguistics); The Internet; Interoperability; Key (lock)","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":[],"consensus_categories":[],"category_scores_codex":[0.0003880804,0.0002183808,0.0002217586,0.0005969307,0.000521489,0.0002990832,0.003463138,0.0002704789,0.0000271708],"category_scores_gemma":[0.001821545,0.0002329905,0.0000545306,0.002258212,0.0001556683,0.000432306,0.0005398429,0.001097692,0.00004793025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002872946,"about_ca_system_score_gemma":0.000154755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003876944,"about_ca_topic_score_gemma":0.0006838206,"domain_scores_codex":[0.9980345,0.0001517387,0.0004585553,0.0007208961,0.0002548313,0.0003795221],"domain_scores_gemma":[0.9982247,0.0002841016,0.00008326051,0.001231984,0.0001141781,0.00006177281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008009548,0.0003563688,0.00009648006,0.00002470602,0.00003296634,0.00001543706,0.0005367422,0.01310973,0.001812036,0.02375615,0.000465472,0.9597138],"study_design_scores_gemma":[0.00003184679,0.0001510855,0.000104695,0.0001270318,0.000006917021,0.000005791545,0.0002936747,0.4758964,0.1814189,0.3415057,0.0002551421,0.0002027977],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0214489,0.00004531742,0.9627286,0.01366223,0.000833454,0.0002877276,0.000004489266,0.0006703038,0.0003189775],"genre_scores_gemma":[0.9411199,0.0001345571,0.0583357,0.000264651,0.0000143862,0.00006206994,0.000001084918,0.000009790441,0.00005783849],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.959511,"threshold_uncertainty_score":0.9501079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421824515450956,"score_gpt":0.3255538070190496,"score_spread":0.2913355618645401,"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."}}