{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.002381334,0.0004149373,0.0007862648,0.0006927124,0.0001081904,0.001084884,0.01486963,0.0005141263,0.00001203489],"category_scores_gemma":[0.006574753,0.0004238806,0.0001404663,0.0001498131,0.0002038227,0.0004854259,0.08620103,0.003603576,0.000001072272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001707297,"about_ca_system_score_gemma":0.0002766636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001781299,"about_ca_topic_score_gemma":0.00002883573,"domain_scores_codex":[0.9967296,0.0004712837,0.0009867629,0.0007603213,0.0005679021,0.0004841455],"domain_scores_gemma":[0.9962453,0.0003374244,0.001155722,0.001792162,0.0003327908,0.00013659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001164844,0.0007919215,0.002174473,0.003477521,0.001553123,0.00884779,0.04572466,0.003836795,0.004083048,0.004078008,0.03971704,0.8855991],"study_design_scores_gemma":[0.0006596986,0.0002217942,0.0003795069,0.006604898,0.00002614119,0.001939918,0.002740498,0.9481012,0.009271562,0.02778061,0.001498051,0.000776078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7405758,0.02784721,0.1530581,0.07142852,0.004574372,0.0002742538,0.000004157635,0.0006510487,0.0015865],"genre_scores_gemma":[0.906027,0.0106288,0.08298053,0.0001308322,0.000103502,0.000003299925,0.000004229867,0.00003044811,0.00009132455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9442645,"threshold_uncertainty_score":0.9999521,"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."}}