{"id":"W3144496954","doi":"10.1109/jiot.2021.3072611","title":"Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges","year":2021,"lang":"en","type":"preprint","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"National Research Foundation of Korea; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Scalability; Edge device; Mobile edge computing; Context (archaeology); Data sharing; Blockchain; Enhanced Data Rates for GSM Evolution; Information privacy; Edge computing; Mobile device; Computer security; Distributed computing; Data science; Cloud computing; World Wide Web; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002979198,0.0003868439,0.0008700491,0.0004598073,0.001382126,0.003654307,0.001878652,0.002610992,0.004828567],"category_scores_gemma":[0.005334401,0.000299411,0.0004097785,0.001344622,0.001902994,0.009361315,0.003267977,0.00299128,0.001031224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001305959,"about_ca_system_score_gemma":0.002234376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379767,"about_ca_topic_score_gemma":0.001676995,"domain_scores_codex":[0.9982658,0.0006517678,0.00007372994,0.0002912202,0.0004408353,0.0002766219],"domain_scores_gemma":[0.9969316,0.001734679,0.0001580299,0.0005353384,0.0003683709,0.0002719488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001580558,0.000127598,0.001112442,0.0004140933,0.00002813232,0.0003698653,0.0002341764,0.08680032,0.001878819,0.7243893,0.01059226,0.173895],"study_design_scores_gemma":[0.00002621098,0.00007231607,0.0001851646,0.0001154364,0.00001001075,0.0001638796,0.0002047213,0.371666,0.001552616,0.5873876,0.0385881,0.0000280754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06829317,0.02150496,0.8158938,0.02982338,0.001018586,0.0002551914,0.0002576962,0.0007085663,0.06224455],"genre_scores_gemma":[0.8654428,0.01602361,0.1032333,0.001514329,0.0007639304,0.0002102529,0.000341709,0.00008474013,0.0123853],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004828567,"threshold_uncertainty_score":0.01615316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024586370004752,"score_gpt":0.2914397041661931,"score_spread":0.1889810671657179,"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."}}