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

Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential for researchers and developers.

Song Guo (Author), Zhihao Qu (Author)

9781108832373, Cambridge University Press

Hardback, published 10 February 2022

228 pages
25.1 x 17.6 x 1.7 cm, 0.54 kg

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.

1. Introduction
2. Preliminary
3. Fundamental Theory and Algorithms of Edge Learning
4. Communication-Efficient Edge Learning
5. Computation Acceleration
6. Efficient Training with Heterogeneous Data Distribution
7. Security and Privacy Issues in Edge Learning Systems
8. Edge Learning Architecture Design for System Scalability
9. Incentive Mechanisms in Edge Learning Systems
10. Edge Learning Applications.

Subject Areas: Machine learning [UYQM], Computer networking & communications [UT], Data analysis: general [GPH]