Edge Learning for Distributed Big Data Analytics

Theory, Algorithms, and System Design

By: Song Guo, Zhihao Qu

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Hardcover | 10 February 2022

At a Glance

Hardcover
230 Pages

Edition Type
New edition

Dimensions(cm)
24.41 x 16.99 x 1.42

Hardcover

RRP $133.95

$121.75

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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.

Industry Reviews

'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

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