{"id":"W4410985938","doi":"10.1109/jiot.2025.3576225","title":"Investigation of the Robustness of XAI-Based Federated Learning Against Adversarial Attacks for Smart Grid False Data Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Robustness (evolution); Adversarial system; Grid; Data mining; Smart grid; Computer security; Artificial intelligence; Data modeling; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.002066839,0.0001829711,0.0003669444,0.0002805838,0.0002308705,0.000130779,0.00220712,0.0001423892,0.000004080017],"category_scores_gemma":[0.001702275,0.0001497014,0.0001806634,0.0005048718,0.0001737243,0.0009982953,0.0004674467,0.0008005755,3.068583e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058531,"about_ca_system_score_gemma":0.0003791307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001312618,"about_ca_topic_score_gemma":0.00002557998,"domain_scores_codex":[0.9976916,0.0004210233,0.0008752574,0.0003261042,0.0004658492,0.0002201967],"domain_scores_gemma":[0.9968088,0.0004889044,0.001537381,0.000513122,0.0005982226,0.0000536279],"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":[0.0003923187,0.00005257937,0.006263569,0.0002633559,0.0002296062,0.000002362494,0.001168247,0.8997185,0.06954164,0.000271134,0.00108197,0.02101467],"study_design_scores_gemma":[0.001116636,0.0001158008,0.0005361375,0.0005985905,0.00005037461,0.000009220304,0.0000697338,0.8414941,0.1553924,0.0002540972,0.0002548922,0.0001079282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2608108,0.00002485008,0.734706,0.0003898452,0.003799781,0.0001772559,0.000003107058,0.0000279774,0.00006038534],"genre_scores_gemma":[0.9738784,0.000003258235,0.02567794,0.0001218195,0.0001897074,0.000003386715,0.00000798531,0.00001430636,0.0001031785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7130676,"threshold_uncertainty_score":0.6104648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997137786689699,"score_gpt":0.2812312948145103,"score_spread":0.2512599169476133,"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."}}