{"id":"W4412448367","doi":"10.1016/j.segan.2025.101794","title":"Personalized poison attack tolerant federated learning for residential load forecasting","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer security; Computer science; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0003389167,0.0002916924,0.0003357443,0.0001262196,0.0007295978,0.0002647237,0.0001055828,0.0002121626,0.00002170738],"category_scores_gemma":[0.00008437982,0.0002949395,0.0001122823,0.0003893707,0.00005455668,0.0001905764,0.00007918794,0.0002758755,2.315369e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652172,"about_ca_system_score_gemma":0.00008469372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002833209,"about_ca_topic_score_gemma":0.000141322,"domain_scores_codex":[0.9983497,0.0000403561,0.0003278932,0.0003248767,0.0001280048,0.0008291984],"domain_scores_gemma":[0.9993287,0.0002079879,0.00005240699,0.0001068108,0.0002016544,0.0001024675],"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":[0.0001487248,0.00001055571,0.0002165891,0.0002719825,0.0001279245,0.00004244459,0.000174725,0.9505255,0.00008812537,0.01395029,0.007273665,0.02716952],"study_design_scores_gemma":[0.0009659876,0.00006446591,0.00003960585,0.000176666,0.00004257806,0.000009417568,0.0007795773,0.826295,0.0002724129,0.0004349508,0.1706276,0.0002917894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.231795,0.02637059,0.6983076,0.0002311975,0.001991349,0.0004259359,0.000003949189,0.001033727,0.03984062],"genre_scores_gemma":[0.9728404,0.0004468116,0.0002890561,0.00009893408,0.0005312751,0.00008444399,0.00005354262,0.00005818105,0.0255974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7410453,"threshold_uncertainty_score":0.9999503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00903893137782898,"score_gpt":0.2202692940660539,"score_spread":0.2112303626882249,"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."}}