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

This book provides an in-depth exploration of machine learning,edge computing,and big data,covering training models,challenges,techniques,architectures,and more. It is a valuable resource for researchers and developers.

Format: Hardback
Length: 228 pages
Publication date: 10 February 2022
Publisher: Cambridge University Press

Discover this multi-disciplinary and insightful work, which seamlessly integrates machine learning, edge computing, and big data. Presents the fundamental principles of training machine learning models, delves into the critical challenges and complexities, and offers comprehensive techniques encompassing edge learning algorithms and system design considerations. Explores architectures, frameworks, and key technologies for enhancing learning performance, ensuring security, and safeguarding privacy, as well as addressing incentive issues in training/inference at the network edge. Designed to ignite fruitful discussions, inspire innovative research ideas, and provide valuable insights to readers from both academia and industry backgrounds. An indispensable resource for experienced researchers and developers, as well as those embarking on their journey into the field of machine learning and its applications.

Unveiling the Power of Machine Learning, Edge Computing, and Big Data Integration:
This groundbreaking work delves into the realm of machine learning, edge computing, and big data integration, offering a comprehensive and interdisciplinary perspective. It serves as a valuable resource for researchers, developers, and practitioners seeking to harness the full potential of these cutting-edge technologies.

In the first chapter, the authors provide an introduction to machine learning, edge computing, and big data, highlighting their interdependencies and the opportunities they present for innovative solutions. They discuss the basics of training machine learning models, emphasizing the challenges and issues involved in developing accurate and efficient algorithms. Comprehensive techniques, such as edge learning algorithms, are introduced to address the unique requirements of distributed computing environments at the edge.

The second chapter delves into the architecture, frameworks, and key technologies for enhancing learning performance, security, and privacy. The authors explore various architectures, including distributed and centralized models, and discuss the trade-offs between scalability, performance, and resource efficiency. They also discuss the importance of frameworks in simplifying the development of machine learning applications and provide insights into popular frameworks such as TensorFlow, PyTorch, and Keras.

In the third chapter, the authors focus on architectures, frameworks, and key technologies for enhancing learning performance, security, and privacy. The authors explore various architectures, including distributed and centralized models, and discuss the trade-offs between scalability, performance, and resource efficiency. They also discuss the importance of frameworks in simplifying the development of machine learning applications and provide insights into popular frameworks such as TensorFlow, PyTorch, and Keras.

In the fourth chapter, the authors delve into the incentive issues in training/inference at the network edge. They discuss the challenges associated with distributed training and the need for efficient and effective incentive mechanisms to promote collaboration and fairness among participants. The authors explore various incentive mechanisms, such as reward sharing, reputation systems, and game theory, and their applications in training/inference at the network edge.

The final chapter summarizes the key insights and takeaways from the previous chapters and highlights the potential applications of machine learning, edge computing, and big data integration in various industries. The authors provide examples of successful implementations and discuss the future directions and challenges in this field.

This work is an essential resource for experienced researchers and developers who are interested in advancing their knowledge and expertise in machine learning, edge computing, and big data integration. It offers a comprehensive and up-to-date overview of the latest trends, techniques, and technologies, enabling readers to stay ahead of the curve. Whether you are a seasoned professional or just starting your journey in this field, this book will provide you with the foundational knowledge and skills necessary to excel in your endeavors.

Weight: 538g
Dimension: 176 x 251 x 21 (mm)
ISBN-13: 9781108832373
Edition number: New ed