Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design

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.


About the Author
Guo, Song: - 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.Qu, Zhihao: - 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.