{"id":"W4411949976","doi":"10.1109/iwcmc65282.2025.11059496","title":"Multi-Criteria Clustering and Client Selection for Heterogeneous Federated Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Cluster analysis; Selection (genetic algorithm); Federated learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004629417,0.001173758,0.002320475,0.001543327,0.001348955,0.001728152,0.002984445,0.001668422,0.001580072],"category_scores_gemma":[0.00757418,0.0005490907,0.0009831645,0.001955315,0.0009310548,0.002130834,0.002507519,0.0012413,0.000589898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002241052,"about_ca_system_score_gemma":0.002264227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004724065,"about_ca_topic_score_gemma":0.004470281,"domain_scores_codex":[0.9963065,0.001475155,0.0001913458,0.0008256347,0.0007705187,0.0004307692],"domain_scores_gemma":[0.995903,0.00166452,0.0003870025,0.0008117209,0.0008770378,0.000356778],"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":[0.0003835131,0.0003790732,0.003388181,0.00009105587,0.0001461849,0.0001766065,0.0002025307,0.8079624,0.002720393,0.009046953,0.00271092,0.1727922],"study_design_scores_gemma":[0.000007245746,0.00002237153,0.0001210367,0.000003073146,0.000004952997,0.0000219659,0.00002494099,0.9945697,0.0006517967,0.004384074,0.0001829183,0.000005978268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03149992,0.0001712273,0.9660098,0.0002197534,0.00002518982,0.0001014979,0.00006472938,0.0008841347,0.001023801],"genre_scores_gemma":[0.8083908,0.00008315959,0.188781,0.0001862652,0.00003362362,0.0001585905,0.0002361762,0.00009955975,0.002030875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004724065,"threshold_uncertainty_score":0.02448303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03364322905403605,"score_gpt":0.3117686041468194,"score_spread":0.2781253750927833,"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."}}