Federated learning for wireless networks / Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han.
2021
Q325.5
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Title
Federated learning for wireless networks / Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han.
Author
Hong, Choong Seon, author.
ISBN
9789811649639 (electronic bk.)
9811649634 (electronic bk.)
9811649626
9789811649622
9811649634 (electronic bk.)
9811649626
9789811649622
Imprint
Singapore : Springer, 2021.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-981-16-4963-9 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
Bibliography, etc. Note
Includes bibliographical references.
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Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 11, 2022).
Added Author
Khan, Latif U. author.
Chen, Mingzhe, author.
Chen, Dawei, author.
Saad, Walid, author.
Han, Zhu, 1974- author.
Chen, Mingzhe, author.
Chen, Dawei, author.
Saad, Walid, author.
Han, Zhu, 1974- author.
Series
Wireless networks (Springer (Firm)), 2366-1445
Available in Other Form
Print version: 9789811649622
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Table of Contents
Part 1 Fundamentals and Background
1 Introduction
2 Fundamentals of Federated Learning
Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning
4 Incentive Mechanisms for Federated Learning
5 Security and Privacy
6 Unsupervised Federated Learning
Part 3 Federated Learning Applications in Wireless Networks
7 Wireless Virtual Reality
8 Vehicular Networks and Autonomous Driving Cars
9 Smart Industries and Intelligent Reflecting Surfaces.
1 Introduction
2 Fundamentals of Federated Learning
Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning
4 Incentive Mechanisms for Federated Learning
5 Security and Privacy
6 Unsupervised Federated Learning
Part 3 Federated Learning Applications in Wireless Networks
7 Wireless Virtual Reality
8 Vehicular Networks and Autonomous Driving Cars
9 Smart Industries and Intelligent Reflecting Surfaces.