{"id":"W3128646026","doi":"10.1109/tnse.2021.3056655","title":"FLAS: Computation and Communication Efficient Federated Learning via Adaptive Sampling","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Correctness; Federated learning; Overhead (engineering); Usability; Convergence (economics); Computation; Distributed computing; Filter (signal processing); Distributed learning; Adaptive sampling; Statistic; Artificial intelligence; Machine learning; Data mining; Human–computer interaction; Algorithm","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":[0.00346937,0.001185416,0.001832803,0.0009560676,0.0009866252,0.001565484,0.003655316,0.001751215,0.0029074],"category_scores_gemma":[0.00892374,0.0004780083,0.0009032858,0.00129252,0.001528014,0.003191574,0.003437503,0.002104267,0.0008797265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001318753,"about_ca_system_score_gemma":0.002529472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004396601,"about_ca_topic_score_gemma":0.005330595,"domain_scores_codex":[0.9977713,0.0007559495,0.0001216315,0.0004581661,0.0006028823,0.000290046],"domain_scores_gemma":[0.9968317,0.001314954,0.0002112511,0.0009099269,0.0005247913,0.0002073312],"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":[0.0008244318,0.000461957,0.002259562,0.0001564679,0.0001665324,0.000196495,0.00017715,0.6241674,0.005704681,0.02310587,0.008494553,0.334285],"study_design_scores_gemma":[0.00002218084,0.00003616096,0.00005696177,0.000004009012,0.00000560989,0.00002327744,0.00001001783,0.9917243,0.000834898,0.006915346,0.0003616163,0.000005712646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01243628,0.0002404727,0.9834527,0.0002280141,0.00006235355,0.00007818695,0.00007458331,0.002540416,0.0008870212],"genre_scores_gemma":[0.6965743,0.0002232407,0.2983907,0.0005714943,0.0001150763,0.0003257492,0.0004832394,0.0002061133,0.003110063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004396601,"threshold_uncertainty_score":0.01834798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387405128044365,"score_gpt":0.2452208174266214,"score_spread":0.2213467661461777,"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."}}