Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design
- Length: 225 pages
- Edition: 1
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2022-01-31
- ISBN-10: 1108832377
- ISBN-13: 9781108832373
- Sales Rank: #0 (See Top 100 Books)
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.
Frontmatter Contents List_of_Figures List_of_Tables Introduction Preliminary Fundamental_Theory_and_Algorithms_of_Edge_Learning Communication-Efficient_Edge_Learning Computation_Acceleration Efficient_Training_with_Heterogeneous_Data_Distribution Security_and_Privacy_Issues_in_Edge_Learning_Systems Edge_Learning_Architecture_Design_for_System_Scalability Incentive_Mechanisms_in_Edge_Learning_Systems Edge_Learning_Applications Bibliography Index
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