Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design

Description

Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field. 00.0 01.0_pp_i_iv_Frontmatter 02.0_pp_v_viii_Contents 03.0_pp_ix_x_List_of_Figures 04.0_pp_xi_xii_List_of_Tables 05.0_pp_1_10_Introduction 06.0_pp_11_23_Preliminary 07.0_pp_24_41_Fundamental_Theory_and_Algorithms_of_Edge_Learning 08.0_pp_42_72_Communication-Efficient_Edge_Learning 09.0_pp_73_97_Computation_Acceleration 10.0_pp_98_111_Efficient_Training_with_Heterogeneous_Data_Distribution 11.0_pp_112_130_Security_and_Privacy_Issues_in_Edge_Learning_Systems 12.0_pp_131_158_Edge_Learning_Architecture_Design_for_System_Scalability 13.0_pp_159_170_Incentive_Mechanisms_in_Edge_Learning_Systems 14.0_pp_171_189_Edge_Learning_Applications 15.0_pp_190_214_Bibliography 16.0_pp_215_218_Index