{"id":"W4360605324","doi":"10.1109/icnc57223.2023.10074237","title":"Towards Instant Clustering Approach for Federated Learning Client Selection","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Cluster analysis; DBSCAN; Task (project management); Machine learning; Set (abstract data type); Selection (genetic algorithm); Quality (philosophy); Server; Artificial intelligence; Data mining; Correlation clustering; CURE data clustering algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003176404,0.0009079697,0.00176406,0.001148579,0.001713706,0.002052791,0.004123965,0.001938971,0.001950534],"category_scores_gemma":[0.006277813,0.0004843287,0.0008605927,0.001813503,0.0009630788,0.002944975,0.003316671,0.001833701,0.001148527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001791345,"about_ca_system_score_gemma":0.002488003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00361211,"about_ca_topic_score_gemma":0.003903687,"domain_scores_codex":[0.9961845,0.001035157,0.0001904831,0.001038708,0.0009962657,0.000554838],"domain_scores_gemma":[0.9963367,0.0008714572,0.0002513569,0.001218691,0.0009755829,0.0003462601],"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":[0.001154193,0.0006041224,0.004693094,0.0001790292,0.0001606207,0.0005283738,0.0006241715,0.4698727,0.01463438,0.060732,0.01573325,0.4310841],"study_design_scores_gemma":[0.00001732498,0.0000348931,0.0001877271,0.000005517801,0.000009135395,0.00009768139,0.00007127519,0.9808523,0.003364125,0.01375414,0.001592298,0.00001355331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01938139,0.0002317085,0.9763023,0.0003401243,0.00005689595,0.00009916945,0.00008021334,0.002272326,0.001235827],"genre_scores_gemma":[0.6504956,0.0002265174,0.3427215,0.0004273821,0.00009916346,0.0001982387,0.0004730404,0.0002208248,0.005137772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004123965,"threshold_uncertainty_score":0.01679862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0572777037353329,"score_gpt":0.2950020808579119,"score_spread":0.237724377122579,"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."}}