{"id":"W4404132848","doi":"10.1109/tnsm.2024.3493758","title":"FeD-TST: Federated Temporal Sparse Transformers for QoS Prediction in Dynamic IoT Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"CHIST-ERA; Agence Nationale de la Recherche","keywords":"Computer science; Quality of service; Computer network; Transformer; Internet of Things; Distributed computing; Embedded system; Electrical engineering; Voltage","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":[0.0007728051,0.0006347298,0.0006035693,0.0004737179,0.000345142,0.0007138025,0.001010054,0.0005532575,0.0009296878],"category_scores_gemma":[0.00204203,0.0002012945,0.0004828796,0.0005820004,0.0004007725,0.001415097,0.0007741672,0.0006651196,0.0001762621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007133989,"about_ca_system_score_gemma":0.0008828528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01550776,"about_ca_topic_score_gemma":0.01237728,"domain_scores_codex":[0.9997832,0.00005266296,0.00001519067,0.00005068451,0.00006255302,0.00003577383],"domain_scores_gemma":[0.9995858,0.0001824528,0.00005000424,0.00005392452,0.0000911688,0.00003655926],"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":[0.0001839654,0.00007321918,0.002115731,0.00003383686,0.00003317935,0.0001179683,0.0000545531,0.9043649,0.003078145,0.004382915,0.001665576,0.08389602],"study_design_scores_gemma":[0.000002259598,0.000006112751,0.00005869245,8.919673e-7,0.000001588621,0.0000084737,0.000004949987,0.9985372,0.0003255507,0.0009630299,0.00008965615,0.000001553986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04732865,0.0001864391,0.9493,0.0001822258,0.00005189099,0.00004790106,0.0002324877,0.001694323,0.0009761895],"genre_scores_gemma":[0.9074052,0.0001524412,0.09128753,0.00007569227,0.00002643927,0.0000487019,0.0004018961,0.00005844327,0.000543623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01550776,"threshold_uncertainty_score":0.03083497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194967014220309,"score_gpt":0.2289951120752679,"score_spread":0.2170454419330648,"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."}}