{"id":"W4387790169","doi":"10.1109/tmc.2023.3325334","title":"Accelerating and Securing Blockchain-Enabled Distributed Machine Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; Public Safety Canada; Western Canada Research Grid; Compute Canada","keywords":"Computer science; Blockchain; Latency (audio); Server; Proof-of-work system; Distributed computing; Artificial intelligence; Theoretical computer science; Computer network; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003989619,0.0007039942,0.001033201,0.0006832363,0.001397887,0.001321888,0.002023259,0.001162226,0.001564527],"category_scores_gemma":[0.01293119,0.000474476,0.0004412248,0.0008900354,0.001343309,0.004537803,0.004456541,0.001783463,0.0005511391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046473,"about_ca_system_score_gemma":0.003033928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883026,"about_ca_topic_score_gemma":0.001929665,"domain_scores_codex":[0.9963021,0.00113754,0.0002317013,0.0005192194,0.001241825,0.0005675399],"domain_scores_gemma":[0.9878237,0.004361769,0.001177722,0.004738032,0.001338469,0.0005603529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001112361,0.000440377,0.006241706,0.0002494907,0.0001225985,0.0007423044,0.0008691371,0.6175352,0.03823092,0.08540807,0.006670687,0.242377],"study_design_scores_gemma":[0.00004385183,0.00006705105,0.0001363588,0.000007218071,0.000006393703,0.00004543209,0.00002849365,0.9741657,0.007437282,0.01686085,0.001191402,0.000009984789],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2012895,0.0004900478,0.7880107,0.001238971,0.0001901747,0.0004236721,0.0001712112,0.004070352,0.004115404],"genre_scores_gemma":[0.9518033,0.0001280904,0.04626725,0.00009622626,0.00003007397,0.0001475453,0.0001364327,0.00005729188,0.001333844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003989619,"threshold_uncertainty_score":0.02109939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423290991377821,"score_gpt":0.2412534006976399,"score_spread":0.2270204907838617,"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."}}