{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001939033,0.001346375,0.001139141,0.001445191,0.0009975429,0.001370311,0.001492786,0.001351813,0.002042588],"category_scores_gemma":[0.004789975,0.0003849763,0.0006900603,0.000751722,0.0006902767,0.002827801,0.003092225,0.001459921,0.002088744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009838954,"about_ca_system_score_gemma":0.001292495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004282177,"about_ca_topic_score_gemma":0.004699144,"domain_scores_codex":[0.9988974,0.0002746028,0.00006183665,0.0003534384,0.0001930455,0.0002196527],"domain_scores_gemma":[0.9979851,0.0005405722,0.0002079023,0.0004712162,0.0006405589,0.0001545901],"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":[0.001148028,0.002507164,0.0280934,0.0001774823,0.0002699937,0.000562303,0.0004621694,0.145812,0.02165978,0.002390868,0.008026171,0.7888907],"study_design_scores_gemma":[0.00003239598,0.0002245791,0.002439274,0.00002518689,0.00004755643,0.0002635781,0.0001618075,0.9762657,0.01390706,0.005024064,0.001574953,0.00003384267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4782176,0.0007573194,0.4858637,0.000974057,0.0002075409,0.0002905038,0.0004768264,0.02592687,0.007285641],"genre_scores_gemma":[0.9388804,0.00008910663,0.05685835,0.0002885783,0.00004485993,0.00007285632,0.0005374897,0.00008389527,0.003144508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004282177,"threshold_uncertainty_score":0.01025468,"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."}}