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This book provides a comprehensive study of Federated Learning (FL) over wireless networks. It consists of three main parts: (a) Fundamentals and preliminaries of FL, (b) analysis and optimization of FL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In the second part of this book, the authors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation...

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...