{"id":"W4416962853","doi":"10.1109/pst65910.2025.11268856","title":"Multilingual Phishing Email Detection Using Lightweight Federated Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Federated learning; Phishing; Construct (python library); Class (philosophy); Work (physics)","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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001242676,0.0005429032,0.0004902464,0.00075238,0.003136612,0.003270007,0.0006938124,0.0005368664,0.0001451322],"category_scores_gemma":[0.0006375709,0.000581249,0.0002612239,0.002824899,0.00009777577,0.001886891,0.0005277243,0.001378213,0.0001016523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000536408,"about_ca_system_score_gemma":0.0004438351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002078056,"about_ca_topic_score_gemma":0.0006588097,"domain_scores_codex":[0.9957721,0.0005878214,0.0008912027,0.001343562,0.0005473849,0.0008578813],"domain_scores_gemma":[0.9981111,0.0003128049,0.0003565453,0.0005357319,0.0004915833,0.0001922011],"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":[0.0001403308,0.0002220228,0.001888724,0.0001800433,0.0002545137,0.00005207612,0.003287737,0.01478197,0.1686623,0.0009622331,0.00009845446,0.8094696],"study_design_scores_gemma":[0.0005956801,0.0001303959,0.000501663,0.0002706703,0.00006832978,0.00003192476,0.0002150739,0.6939469,0.2998492,0.000322784,0.003637041,0.0004302937],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4304339,0.0005726491,0.5552366,0.0002651942,0.00652197,0.0002614763,4.098883e-7,0.000588262,0.006119512],"genre_scores_gemma":[0.9872232,0.00005735879,0.005346693,0.0003458979,0.0005631886,0.00000770069,0.000001927212,0.00003459336,0.006419429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8090392,"threshold_uncertainty_score":0.9996639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107510251092337,"score_gpt":0.2754471795324671,"score_spread":0.2543720770215437,"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."}}