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

Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design New Edition book cover

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

Author(s): Song Guo (Author), Zhihao Qu (Author)

  • Publisher: Cambridge University Press
  • Publication Date: 10 Feb. 2022
  • Edition: New
  • Language: English
  • Print length: 228 pages
  • ISBN-10: 1108832377
  • ISBN-13: 9781108832373

Book 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.

Editorial Reviews

Review

‘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

Book Description

Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential for researchers and developers.

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