Edge Learning for Distributed Big Data Analytics

Theory, Algorithms, and System Design

Inbunden, Engelska, 2022

Av Song Guo, Zhihao Qu, Song (The Hong Kong Polytechnic University) Guo, Zhihao (The Hong Kong Polytechnic University) Qu

1 199 kr

Beställningsvara. Skickas inom 7-10 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2022-02-10
Mått176 x 251 x 17 mm
Vikt540 g
FormatInbunden
SpråkEngelska
Antal sidor228
FörlagCambridge University Press
ISBN9781108832373

Tillhör följande kategorier

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.

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.