{"id":"W4411336765","doi":"10.1109/jiot.2025.3580378","title":"Toward Efficient Federated Load Forecasting: Personalization Mechanisms and Their Impact","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Personalization; Load management; Distributed computing; Computer network; World Wide Web","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":[0.004510805,0.0008974894,0.001154575,0.0007491265,0.0007131617,0.001738249,0.001679339,0.001211525,0.0008294478],"category_scores_gemma":[0.0117504,0.0003780783,0.0004877988,0.001108316,0.0008670164,0.00446985,0.00228278,0.001930781,0.0003691197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000661403,"about_ca_system_score_gemma":0.0009392031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002170707,"about_ca_topic_score_gemma":0.002216097,"domain_scores_codex":[0.9981396,0.0005701822,0.0001080553,0.0005913512,0.0003610322,0.0002298245],"domain_scores_gemma":[0.9931147,0.002347084,0.0005380602,0.003072066,0.0006784052,0.0002497594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009129339,0.000874573,0.01562707,0.0001105737,0.0002138939,0.0002092693,0.0004706472,0.5167691,0.01138915,0.01238681,0.003933911,0.437102],"study_design_scores_gemma":[0.00002490488,0.00009054418,0.001561618,0.00001770739,0.00002651492,0.0001069539,0.00009362019,0.9762425,0.005363373,0.01503629,0.001413694,0.00002219084],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1475673,0.0007508643,0.844476,0.0008374798,0.0001283266,0.0001012151,0.0001862151,0.003442241,0.002510312],"genre_scores_gemma":[0.9337195,0.0001797182,0.06495639,0.0001757219,0.00007311757,0.00004687242,0.0001422925,0.0000637113,0.000642626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004510805,"threshold_uncertainty_score":0.02385569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01808902386511395,"score_gpt":0.2306685755740908,"score_spread":0.2125795517089769,"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."}}