Mingzhe Chen's Books

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

Write a review Read reviews Take a Quiz Solve Book Puzzle

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

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.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

257

Publisher

Springer Nature

Published Date

ISBN 10

9811649634

ISBN 13

9789811649639

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

Write a review Read reviews Take a Quiz Solve Book Puzzle

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

257

Publisher

Springer Nature

Published Date

ISBN 10

9811649634

ISBN 13

9789811649639

WIRELESS FOR MACHINE LEARNING

A Survey

By: Henrik Hellström,José Mairton B. da Silva Jr.,Mohammad Mohammadi Amiri,Mingzhe Chen,Viktoria Fodor,H. Vincent Poor,Carlo Fischione

Write a review Read reviews Take a Quiz Solve Book Puzzle

WIRELESS FOR MACHINE LEARNING

A Survey

By: Henrik Hellström,José Mairton B. da Silva Jr.,Mohammad Mohammadi Amiri,Mingzhe Chen,Viktoria Fodor,H. Vincent Poor,Carlo Fischione

This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. It gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

124

Published Date

ISBN 10

163828007X

ISBN 13

9781638280071

Book's by Mingzhe Chen

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

WIRELESS FOR MACHINE LEARNING

WIRELESS FOR MACHINE LEARNING

Username

Password

Username

Password

Password again

Birthday

Email