{"id":"W7129089335","doi":"10.1109/iemcon67450.2025.11381192","title":"A Comparative Study of Federated Learning and Synthetic Data for Privacy-Aware Machine Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Federated learning; Synthetic data; Benchmark (surveying); Scarcity; Distributed learning; Information privacy","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":[0.01049234,0.0008374678,0.0009663887,0.0008817003,0.0007016958,0.001480076,0.001464747,0.001055895,0.000542271],"category_scores_gemma":[0.02276074,0.0002216506,0.000567821,0.001141654,0.001374371,0.002538645,0.001482188,0.001278596,0.0001649956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00129113,"about_ca_system_score_gemma":0.001379917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833577,"about_ca_topic_score_gemma":0.001396456,"domain_scores_codex":[0.9941451,0.00329508,0.0003381512,0.0009067626,0.0009526046,0.0003623276],"domain_scores_gemma":[0.977504,0.01317694,0.001051716,0.00581438,0.001895242,0.0005578127],"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":[0.0009068078,0.0007595183,0.008948331,0.0002218025,0.000150265,0.0001584098,0.0001697738,0.8991734,0.003217158,0.007312702,0.001683785,0.07729795],"study_design_scores_gemma":[0.00004290913,0.0003542301,0.001415982,0.00001919218,0.00001597105,0.0001019263,0.0001233978,0.9850206,0.00618007,0.005870814,0.0008404878,0.00001439571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7477135,0.00114912,0.243045,0.001099514,0.0001872816,0.0002914913,0.0006398035,0.002372939,0.003501358],"genre_scores_gemma":[0.9672642,0.0001192749,0.03161127,0.00009801304,0.00001752236,0.00006705463,0.0005281027,0.00002962597,0.0002648384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01049234,"threshold_uncertainty_score":0.05548948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07897672897987701,"score_gpt":0.3555294751865868,"score_spread":0.2765527462067098,"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."}}