{"id":"W4416118711","doi":"10.48550/arxiv.2504.05153","title":"SparsyFed: Sparse Adaptive Federated Training","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; Royal Academy of Engineering; Institute for Catastrophic Loss Reduction","keywords":"Federated learning; Key (lock); Overhead (engineering); Hyperparameter; Training set; Sparse matrix","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002638264,0.001085491,0.001115387,0.0004890253,0.0007419597,0.001184065,0.002569509,0.002023933,0.004369929],"category_scores_gemma":[0.01322544,0.0005712403,0.0006665493,0.0007037018,0.001350617,0.002700676,0.003353591,0.002788003,0.001508007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007631308,"about_ca_system_score_gemma":0.0017903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003369649,"about_ca_topic_score_gemma":0.005372125,"domain_scores_codex":[0.998507,0.0005297618,0.00008318684,0.0003685444,0.0003359999,0.0001754436],"domain_scores_gemma":[0.9959066,0.001910764,0.0002248376,0.001349058,0.0004302486,0.0001785004],"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":[0.0006220377,0.0002606569,0.002160842,0.0001933136,0.0001189092,0.0002323293,0.0002731192,0.6478205,0.007592576,0.03065958,0.01787819,0.292188],"study_design_scores_gemma":[0.00002776076,0.00003758826,0.00009098993,0.0000100181,0.000005241215,0.00004190519,0.00002126998,0.9817711,0.002021795,0.01452252,0.001441206,0.000008570622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01047168,0.0001494769,0.9840415,0.0003466012,0.00006758878,0.00007104404,0.0001750164,0.002927685,0.001749483],"genre_scores_gemma":[0.5232918,0.0001959981,0.4672726,0.0009161325,0.0001112902,0.0003853056,0.00120288,0.0006414865,0.005982466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004369929,"threshold_uncertainty_score":0.01461893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1543047633024396,"score_gpt":0.3104315652623048,"score_spread":0.1561268019598652,"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."}}