Song Guo

Edge Learning for Distributed Big Data Analytics (eBook, PDF)

Theory, Algorithms, and System Design

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 31. Dezember 2021
• Englisch
• ISBN-13: 9781108962544
• Artikelnr.: 66183264

Autorenporträt

Song Guo is a Full Professor in the Department of Computing at The Hong Kong Polytechnic University. He is an IEEE Fellow and the Editor-in-Chief of the IEEE Open Journal of the Computer Society. He was a member of the IEEE ComSoc Board of Governors and a Distinguished Lecturer of the IEEE Communications Society.

Inhaltsangabe

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.