{"id":"W3112044954","doi":"10.1609/aaai.v35i9.16960","title":"Personalized Cross-Silo Federated Learning on Non-IID Data","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":659,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Huawei Technologies (Canada); Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Baptist University","keywords":"Computer science; Benchmark (surveying); Heuristic; Pairwise comparison; Convergence (economics); Federated learning; Deep learning; Machine learning; Artificial intelligence; Artificial neural network; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00983317,0.00154883,0.003054118,0.0009695108,0.001599969,0.002904011,0.004659505,0.003118294,0.002217595],"category_scores_gemma":[0.02636062,0.0009825395,0.001204187,0.00165995,0.002636097,0.008488297,0.006140471,0.00502368,0.001234684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001999421,"about_ca_system_score_gemma":0.002212774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002759968,"about_ca_topic_score_gemma":0.00356388,"domain_scores_codex":[0.9948725,0.00220993,0.000248325,0.001487884,0.0007524734,0.0004288622],"domain_scores_gemma":[0.9863999,0.00574797,0.0007594251,0.005396006,0.001173843,0.0005228585],"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":[0.000830785,0.0005772748,0.005654008,0.0001793545,0.0002728576,0.0004271447,0.0005153498,0.7162154,0.003451873,0.03198836,0.008502781,0.2313848],"study_design_scores_gemma":[0.00001979274,0.00005785767,0.0002124649,0.000009077596,0.00001221837,0.00005529391,0.00006793811,0.96231,0.001230476,0.03530399,0.0007095221,0.00001129183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03651975,0.0003797735,0.9588316,0.000976406,0.0000828283,0.0001200096,0.0002197824,0.00169991,0.001169883],"genre_scores_gemma":[0.7601243,0.0002232021,0.2327758,0.0008161676,0.0001483198,0.0002652431,0.0008643629,0.000253279,0.004529359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00983317,"threshold_uncertainty_score":0.05200338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1532262172409678,"score_gpt":0.3559079458792938,"score_spread":0.202681728638326,"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."}}