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

✍ Scribed by Song Guo, Zhihao Qu

Publisher: Cambridge University Press
Year: 2022
Tongue: English
Leaves: 231
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

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

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