{"id":"W4297464455","doi":"10.1155/2022/8425975","title":"Deep Federated Learning Based Convergence Analysis in Relaying-Aided MEC-IoT Networks","year":2022,"lang":"en","type":"article","venue":"Journal of Engineering","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Bit error rate; Wireless; Internet of Things; Convergence (economics); Rate of convergence; Transmission (telecommunications); Deep learning; Wireless network; Computer network; Computer engineering; Channel (broadcasting); Telecommunications; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003778905,0.001098342,0.001367185,0.0009339405,0.0005390245,0.0009855218,0.00156383,0.001519372,0.001802978],"category_scores_gemma":[0.0134269,0.0004295951,0.0007557772,0.000647616,0.001834202,0.001881251,0.001756761,0.001951123,0.0002183162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821875,"about_ca_system_score_gemma":0.001417225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005484401,"about_ca_topic_score_gemma":0.003477115,"domain_scores_codex":[0.999281,0.0002693872,0.00003539089,0.0001162159,0.0001706822,0.000127316],"domain_scores_gemma":[0.9929711,0.005083218,0.0004181293,0.0002338107,0.001099811,0.0001940412],"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":[0.00005087779,0.00002443985,0.0008691258,0.00005085931,0.00002937899,0.00008460339,0.00006432021,0.9667349,0.0004690704,0.02284298,0.00048156,0.008297864],"study_design_scores_gemma":[0.000001389636,0.000006765279,0.00004518846,0.000004627029,0.000002855397,0.000008655317,0.000005575926,0.9949929,0.0001192832,0.004756744,0.00005353504,0.000002532556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0478667,0.0006795673,0.9475224,0.0005076122,0.00004364294,0.00003079254,0.00007649376,0.0002468129,0.003025986],"genre_scores_gemma":[0.9389004,0.000666713,0.05529832,0.0002613551,0.0000478887,0.0001005318,0.0001833648,0.00010031,0.004441239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005484401,"threshold_uncertainty_score":0.01998502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006835054089258681,"score_gpt":0.2054160750182038,"score_spread":0.1985810209289451,"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."}}