{"id":"W4404189337","doi":"10.1145/3703628","title":"<scp>Clipper</scp> : Online Joint Client Sampling and Power Allocation for Wireless Federated Learning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Telefonaktiebolaget LM Ericsson","keywords":"Computer science; Clipper (electronics); Joint (building); Wireless; Federated learning; Computer network; Human–computer interaction; Distributed computing; Operating system; Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003298552,0.0001920986,0.0002528776,0.0003339696,0.0005378245,0.000364858,0.001219455,0.0001330637,5.877802e-7],"category_scores_gemma":[0.001637136,0.0001865304,0.00004852093,0.00035957,0.00004140915,0.000493724,0.0004069555,0.0003453689,0.000001622669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001047386,"about_ca_system_score_gemma":0.0001280677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003436387,"about_ca_topic_score_gemma":0.000003023719,"domain_scores_codex":[0.9979392,0.000148226,0.0005751682,0.0005467802,0.0005325102,0.000258155],"domain_scores_gemma":[0.997654,0.0005796966,0.000151079,0.001072962,0.0004922093,0.0000500876],"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":[0.000003412775,0.00003211833,0.00005122397,0.0002526845,0.0000426879,9.183406e-8,0.0007026838,0.8503982,0.0004847294,0.000122776,0.00004368671,0.1478657],"study_design_scores_gemma":[0.0003559377,0.0002216828,0.0001448791,0.001009438,0.00004393475,0.00001676438,0.00051925,0.9957824,0.0005203346,0.001224799,0.00006193283,0.00009862707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4745438,0.0005285268,0.5235291,0.0004239665,0.0004033543,0.0002784499,0.000006351202,0.0002750125,0.00001148464],"genre_scores_gemma":[0.9405816,0.0002499245,0.05901366,0.00001645331,0.00004148503,0.00003598738,0.00002672938,0.00001944506,0.00001474891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4660378,"threshold_uncertainty_score":0.760649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017510622612408,"score_gpt":0.3335603378095807,"score_spread":0.2318092755483399,"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."}}