Edge Learning for Distributed Big Data Analytics

Theory, Algorithms, and System Design

Inbunden, Engelska, 2022

984 kr

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Beskrivning

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

Utgivningsdatum:2022-02-10
Mått:176 x 251 x 17 mm
Vikt:540 g
Format:Inbunden
Språk:Engelska
Antal sidor:228
Förlag:Cambridge University Press
ISBN:9781108832373

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

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.

Recensioner i media

'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

Innehållsförteckning

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.