Edge Learning for Distributed Big Data Analytics
Theory, Algorithms, and System Design
Published on 10. February 2022
Book
Hardback
228 pages
978-1-108-83237-3 (ISBN)
Description
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.
Reviews / Votes
'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, ChoiceMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Illustrations
Worked examples or Exercises
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 17 mm
Weight
584 gr
ISBN-13
978-1-108-83237-3 (9781108832373)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Song Guo | Zhihao Qu
Edge Learning for Distributed Big Data Analytics
Theory, Algorithms, and System Design
E-Book
03/2022
Cambridge University Press
€63.49
Available for download
Persons
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. Zhihao Qu is an assistant researcher in the School of Computer and Information at Hohai University and in the Department of Computing at The Hong Kong Polytechnic University.
Content
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.