{"id":"W4399333454","doi":"10.1145/3655693.3660252","title":"An Enhanced Combinatorial Contextual Neural Bandit Approach for Client Selection in Federated Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Universitas Brawijaya","keywords":"Computer science; Robustness (evolution); Machine learning; Metric (unit); Artificial intelligence; Selection (genetic algorithm); Context (archaeology); Raw data; Process (computing); Adversary; Data mining; Engineering","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":[0.003205057,0.001100409,0.001566842,0.0006903374,0.0009068974,0.001579316,0.002716985,0.00158593,0.002139245],"category_scores_gemma":[0.01029275,0.0004254293,0.0006700139,0.0009448976,0.001392866,0.00271896,0.002848031,0.001916617,0.000632045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348367,"about_ca_system_score_gemma":0.001487353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002284586,"about_ca_topic_score_gemma":0.003508953,"domain_scores_codex":[0.9973652,0.001160311,0.0001119954,0.0005539593,0.0005055954,0.0003030084],"domain_scores_gemma":[0.9962369,0.001792545,0.0003190892,0.0009613577,0.000467627,0.0002223682],"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":[0.0005775083,0.000286069,0.003874869,0.00009365068,0.0001190066,0.0002534667,0.0001726453,0.8262172,0.004428995,0.02390744,0.003425501,0.1366437],"study_design_scores_gemma":[0.000007626204,0.0000370366,0.0001047554,0.000004763812,0.000008290792,0.00003513568,0.00001216985,0.9908583,0.001016426,0.007665891,0.0002443332,0.000005416966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04993016,0.0003564941,0.9452851,0.0004727051,0.00003860361,0.0001131941,0.0001277442,0.00153876,0.002137228],"genre_scores_gemma":[0.8913617,0.0001511118,0.1048211,0.0004477049,0.00006103839,0.0001879405,0.0002885104,0.0001355975,0.002545267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003205057,"threshold_uncertainty_score":0.01695019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714476807067457,"score_gpt":0.2934488481751975,"score_spread":0.2663040801045229,"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."}}