{"id":"W4290996243","doi":"10.1109/icc45855.2022.9838945","title":"Federated Learning for WiFi Fingerprinting","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer security","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":[0.0002171088,0.0001230492,0.0001144969,0.0002681481,0.0008251379,0.0001164232,0.001041135,0.00004538792,0.0007145887],"category_scores_gemma":[0.0001289959,0.0001500891,0.00006648675,0.0002750586,0.00005652389,0.00009723486,0.000251485,0.0005543556,0.00003273183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888974,"about_ca_system_score_gemma":0.00003885553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001143721,"about_ca_topic_score_gemma":0.00002280502,"domain_scores_codex":[0.9991212,0.00006274067,0.0002620558,0.0001573236,0.0002260435,0.00017056],"domain_scores_gemma":[0.99916,0.0002000047,0.0000742597,0.0003782888,0.0001615781,0.00002586553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005858756,0.0002323127,0.0009253116,0.000033343,0.0002834463,0.000003081328,0.001211947,0.2860784,0.02694956,0.5975705,0.01797793,0.06867556],"study_design_scores_gemma":[0.0002947219,0.00005750884,0.0001014488,0.00001794107,0.000007891259,0.000004804097,0.001286312,0.8576377,0.003114142,0.002651699,0.134616,0.0002098369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08113144,0.0005481291,0.4070553,0.01660695,0.006186299,0.001687628,0.0005317671,0.005831984,0.4804205],"genre_scores_gemma":[0.9955701,0.0002022353,0.001120381,0.000145513,0.00003837549,0.0008752196,0.0002841505,0.00003021333,0.001733867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9144386,"threshold_uncertainty_score":0.7824246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07942833426290921,"score_gpt":0.3190124764215688,"score_spread":0.2395841421586596,"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."}}