{"id":"W3035353486","doi":"","title":"From Local SGD to Local Fixed Point Methods for Federated Learning","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Bottleneck; Computer science; Fixed point; Saddle point; Mathematical optimization; Context (archaeology); Computation; Operator (biology); Saddle; Convergence (economics); Theoretical computer science; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003992518,0.0002450154,0.0002628316,0.0001763913,0.0002124345,0.0004134484,0.000956311,0.00008850271,0.0003439067],"category_scores_gemma":[0.001459432,0.0002472877,0.00009533363,0.0002891062,0.00004625535,0.0002679616,0.0003616457,0.000581141,0.00009905959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163392,"about_ca_system_score_gemma":0.00008206874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570004,"about_ca_topic_score_gemma":0.000005742475,"domain_scores_codex":[0.9980202,0.0002641513,0.0003771145,0.0006981961,0.0003642331,0.0002761564],"domain_scores_gemma":[0.9984888,0.0005123981,0.0001821667,0.0001577283,0.0004248221,0.0002340836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002625168,0.00006282399,0.0002845162,0.000009874116,0.0001052132,0.00001251188,0.002969939,0.3183586,0.005747294,0.2053464,0.0005521856,0.4662881],"study_design_scores_gemma":[0.0004891355,0.0005643157,0.00006120176,0.00005304481,0.000005929494,0.000003235907,0.0001953815,0.9820394,0.004326643,0.004924962,0.00707332,0.0002634432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003318938,0.00001355211,0.979477,0.01647852,0.0003497939,0.0003031723,0.000009814375,0.0006987986,0.002337446],"genre_scores_gemma":[0.5398855,0.000002653837,0.4579564,0.00170804,0.00008692869,0.00005509888,0.00008007597,0.00002003043,0.0002052543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6636808,"threshold_uncertainty_score":0.9999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0574492059463758,"score_gpt":0.363646518265808,"score_spread":0.3061973123194321,"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."}}