Edge Learning for Distributed Big Data Analytics
Edge Learning for Distributed Big Data Analytics
Looking for an inspection copy?
This title is not currently available for inspection.
£67.00
GBPDiscover 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.
- Includes case studies of real-world applications
- Provides both basic and advanced material suitable for both researchers in academia and developers in industry
- Presents the essentials of edge learning, including the basics of model training, key challenges, comprehensive techniques, and future research directions
Reviews & endorsements
'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, Choice
Product details
February 2022Hardback
9781108832373
228 pages
251 × 176 × 17 mm
0.54kg
Available
Table of Contents
- 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.
