Choong Seon Hong - Federated Learning for Wireless Networks

The purpose of Springers Wireless Networks book series is to establish the state of the art and set the course for future research and development in wireless communication networks. The scope of this series includes not only all aspects of wireless networks (including cellular networks, WiFi, sensor networks, and vehicular networks), but related areas such as cloud computing and big data. The series serves as a central source of references for wireless networks research and development. It aims to publish thorough and cohesive overviews on specific topics in wireless networks, as well as works that are larger in scope than survey articles and that contain more detailed background information. The series also provides coverage of advanced and timely topics worthy of monographs, contributed volumes, textbooks and handbooks.

** Indexing: Wireless Networks is indexed in EBSCO databases and DPLB **

More information about this series at http://www.springer.com/series/14180

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A remarkable interest in machine learning-based schemes as key enablers for next-generation intelligent wireless systems has been observed. Most of the existing learning-based solutions rely on centralized training and inference processes. However, these machine learning paradigms based on centralized training result in end users privacy leakage and are infeasible due to large bandwidth requirements for the transfer of the enormous amount of data. Furthermore, these schemes may violate the strict latency constraints of wireless systems. To address these issues, training in a distributed machine learning scheme at the network edge can be one of the promising solutions. Distributed machine learning avoids uploading the end-devices data to a central server for training; this not only helps preserve privacy but also reduces network traffic congestion. Federated learning (FL) is one of the most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, impairments of the wireless channel, such as interference, uncertainties among wireless channel states, and noise will significantly affect the FL performance. For instance, the convergence time of FL is significantly affected by the channel transmission delay. In consequence, wireless network performance optimization is necessary for wireless FL. On the other hand, FL can also be used for solving wireless communication problems and optimizing network performance. The goal of this book is to provide a comprehensive study of federated learning for wireless networks. The book consists of three main parts: (a) Fundamentals and Background of Federated Learning for Wireless Networks, (b) Design and Analysis of Federated Learning Over Wireless Networks, and (c) Federated Learning Applications in Wireless Networks. The first part deals with a brief discussion on the fundamentals of federated learning for wireless networks. In the second part, we comprehensively discuss the design and analysis of wireless federated learning. Specifically, resource optimization, incentive mechanism, security, and privacy are considered. Moreover, we present several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning over wireless networks. In the final part, we present several applications of federated learning in wireless networks.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government(MSIT) (No. No. 2020R1A4A1018607) and by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-01287, Evolvable Deep Learning Model Generation Platform for Edge Computing). Dr. CS Hong (email:cshong@khu.ac.kr) is the corresponding author.