{"id":"W4398152538","doi":"10.1109/tbdata.2024.3403390","title":"Accelerating Blockchain-Enabled Federated Learning With Clustered Clients","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Single point of failure; Blockchain; Asynchronous communication; Distributed computing; Enhanced Data Rates for GSM Evolution; Cluster analysis; Distributed learning; Edge device; Convergence (economics); Artificial intelligence; Machine learning; Data mining; Computer security; Computer network","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":[0.0023336,0.0004942008,0.0008654122,0.0004385729,0.0009239449,0.001242244,0.00187932,0.001034751,0.003053904],"category_scores_gemma":[0.006508613,0.0002701985,0.0003161266,0.0007385031,0.0008754669,0.002591548,0.002842677,0.001349466,0.000718651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000980928,"about_ca_system_score_gemma":0.0027949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004339118,"about_ca_topic_score_gemma":0.004329419,"domain_scores_codex":[0.9985996,0.0003915421,0.00006545903,0.0002995123,0.0003744639,0.0002694337],"domain_scores_gemma":[0.9964954,0.001320901,0.000235435,0.001051499,0.0006077808,0.000289029],"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":[0.0007844164,0.0002667659,0.003907613,0.0001341323,0.00005687693,0.0004009797,0.0003425206,0.7908338,0.009914912,0.02956497,0.004328331,0.1594646],"study_design_scores_gemma":[0.00003732639,0.00004066332,0.0001363881,0.000006370483,0.000004317944,0.00003671852,0.00002796983,0.9864745,0.002634586,0.009735266,0.0008586627,0.000007262267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1697684,0.0004598931,0.818583,0.0008397935,0.0001176444,0.0002551869,0.0003124195,0.003829725,0.005833871],"genre_scores_gemma":[0.9611611,0.00009599792,0.03598515,0.00008982112,0.00001645567,0.0001054647,0.0002014408,0.00004504905,0.002299436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004339118,"threshold_uncertainty_score":0.01234138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008990637863321,"score_gpt":0.2952834803242316,"score_spread":0.1943844165378995,"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."}}