{"id":"W4320732177","doi":"10.3390/s23042112","title":"Reviewing Federated Machine Learning and Its Use in Diseases Prediction","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Computer science; Confidentiality; Health care; Automation; Variety (cybernetics); Artificial intelligence; Machine learning; Investment (military); Information privacy; Data science; Knowledge management; Computer security; Engineering; Economics","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":[0.002201094,0.0005504093,0.0005983368,0.002504128,0.0005217174,0.001959359,0.001006576,0.001679079,0.002127294],"category_scores_gemma":[0.009682545,0.0003114871,0.0007238397,0.004038463,0.0008795542,0.002587797,0.0005058479,0.002023485,0.001807899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167129,"about_ca_system_score_gemma":0.001439636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00318286,"about_ca_topic_score_gemma":0.002830832,"domain_scores_codex":[0.9983777,0.0005348792,0.0001774722,0.0002973518,0.0005559217,0.00005666146],"domain_scores_gemma":[0.9945312,0.003304134,0.0002875346,0.0001976643,0.001557187,0.000122388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005888224,0.00006074504,0.002306269,0.003832767,0.000112816,0.0003577621,0.0002115738,0.008247746,0.0006289757,0.05189973,0.2114612,0.7208214],"study_design_scores_gemma":[0.000005730563,0.000111584,0.003046829,0.004478105,0.00007416681,0.001111681,0.0001385325,0.009798638,0.0008229578,0.03101958,0.9493359,0.000056292],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001902029,0.9020941,0.04002305,0.0249153,0.01413523,0.00006197606,0.0002400583,0.0002832552,0.0163451],"genre_scores_gemma":[0.03222635,0.8940808,0.01945699,0.01326291,0.02893317,0.0001059751,0.0006166879,0.0001097789,0.01120743],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00318286,"threshold_uncertainty_score":0.01164067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1214832096083933,"score_gpt":0.340335484355161,"score_spread":0.2188522747467677,"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."}}