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

Song Guo; Zhihao Qu

doi:10.1017/9781108955959

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.

‘This book does especially well in suggesting thought-provoking future directions in each chapter and in threading together issues of data privacy and human behavior throughout … Highly recommended.’

J. Forrest Source: Choice

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Edge Learning for Distributed Big Data Analytics

Theory, Algorithms, and System Design

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

List of Figures
pp ix-x

List of Tables
pp xi-xii

1 - Introduction
pp 1-10

2 - Preliminary
pp 11-23

3 - Fundamental Theory and Algorithms of Edge Learning
pp 24-41

4 - Communication-Efficient Edge Learning
pp 42-72

5 - Computation Acceleration
pp 73-97

6 - Efficient Training with Heterogeneous Data Distribution
pp 98-111

7 - Security and Privacy Issues in Edge Learning Systems
pp 112-130

8 - Edge Learning Architecture Design for System Scalability
pp 131-158

9 - Incentive Mechanisms in Edge Learning Systems
pp 159-170

10 - Edge Learning Applications
pp 171-189

Bibliography
pp 190-214

Index
pp 215-218

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Edge Learning for Distributed Big Data Analytics

Theory, Algorithms, and System Design

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