{"id":"W4413757687","doi":"10.2139/ssrn.5395281","title":"A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Computation; Finance; Business; Computer security; Programming language","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":[0.003480653,0.0008280704,0.001995749,0.00223094,0.0007808979,0.003482534,0.001921233,0.002238635,0.004921739],"category_scores_gemma":[0.008783666,0.0006750369,0.0008735736,0.005800162,0.00239234,0.007030419,0.002245504,0.003044441,0.001873167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002061932,"about_ca_system_score_gemma":0.00227743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009914516,"about_ca_topic_score_gemma":0.0006778567,"domain_scores_codex":[0.9973181,0.000763252,0.0002810234,0.0004859935,0.0009364008,0.0002152102],"domain_scores_gemma":[0.9941302,0.004242163,0.0002259859,0.0007214047,0.0005568622,0.000123285],"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":[0.0001393683,0.0001221792,0.0004189938,0.006282585,0.00008654188,0.0000915716,0.0001054328,0.01042502,0.001012557,0.3324697,0.01439,0.634456],"study_design_scores_gemma":[0.000045675,0.0002531863,0.0008809685,0.00433203,0.0001061542,0.000726074,0.0001288189,0.03901318,0.003133566,0.3855527,0.5657414,0.00008620521],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003072074,0.8554057,0.1204324,0.004074526,0.00138137,0.00006562995,0.0001081556,0.0002049768,0.01525518],"genre_scores_gemma":[0.1016163,0.8204136,0.06146463,0.001954863,0.005026284,0.0001531928,0.0003235358,0.0001557676,0.008891838],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004921739,"threshold_uncertainty_score":0.0184077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009971770103634616,"score_gpt":0.2626261041102862,"score_spread":0.2526543340066516,"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."}}