{"id":"W3048421505","doi":"10.48550/arxiv.2008.04743","title":"Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Scalability; Blockchain; Computer science; Overhead (engineering); Scheme (mathematics); Distributed computing; Enhanced Data Rates for GSM Evolution; Isolation (microbiology); Computer security; Database; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.0002379246,0.000317143,0.0003699977,0.0001595673,0.0006960371,0.0001936415,0.001590488,0.0005040793,0.00001168818],"category_scores_gemma":[0.00007003619,0.0003401314,0.00007039674,0.0009431241,0.0003279638,0.00005437465,0.002464912,0.001632038,0.00002471811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000107381,"about_ca_system_score_gemma":0.0001423802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000116631,"about_ca_topic_score_gemma":0.00004310134,"domain_scores_codex":[0.9980312,0.0002212996,0.0002015116,0.001109698,0.00008221166,0.0003540959],"domain_scores_gemma":[0.9979402,0.0001834494,0.0002657364,0.001216925,0.0001946238,0.0001990835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004910689,0.0003337796,0.004583563,0.00008009098,0.0001697319,0.00008450296,0.001393762,0.2501404,0.00003907939,0.741859,0.00008658583,0.001180456],"study_design_scores_gemma":[0.0006199025,0.00004206801,0.0008144152,0.0001175752,0.00004194947,0.000006527118,0.0002149368,0.9791406,0.0001391654,0.01788946,0.0005802632,0.0003931671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2890113,0.0003195411,0.7083961,0.001102574,0.00003706279,0.0003811143,0.000004319833,0.0005789803,0.0001690303],"genre_scores_gemma":[0.9504524,0.0003456049,0.04889206,0.0001327808,0.000008626983,0.000007089197,0.00001503459,0.00001936556,0.0001270722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7290002,"threshold_uncertainty_score":0.999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331402343637535,"score_gpt":0.2006379458342374,"score_spread":0.1573239223978621,"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."}}