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 ...
Read More
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 to support the deployment of FL over realistic wireless networks. It also presents several solutions based on optimization theory, graph theory and machine learning to optimize the performance of FL over wireless networks. In the third part of this book, the authors introduce the use of wireless FL algorithms for autonomous vehicle control and mobile edge computing optimization. Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of 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, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms. This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book.
Read Less
Add this copy of Communication Efficient Federated Learning for Wireless to cart. $154.76, new condition, Sold by Ria Christie Books rated 4.0 out of 5 stars, ships from Uxbridge, MIDDLESEX, UNITED KINGDOM, published 2024 by Springer.
Add this copy of Communication Efficient Federated Learning for Wireless to cart. $157.65, like new condition, Sold by GreatBookPricesUK5 rated 4.0 out of 5 stars, ships from Castle Donington, DERBYSHIRE, UNITED KINGDOM, published 2024 by Springer.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Fine. Sewn binding. Cloth over boards. 179 p. Contains: Unspecified, Illustrations, black & white, Illustrations, color, Tables, color. Wireless Networks. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Communication Efficient Federated Learning for Wireless to cart. $159.50, new condition, Sold by GreatBookPricesUK5 rated 4.0 out of 5 stars, ships from Castle Donington, DERBYSHIRE, UNITED KINGDOM, published 2024 by Springer.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. Sewn binding. Cloth over boards. 179 p. Contains: Unspecified, Illustrations, black & white, Illustrations, color, Tables, color. Wireless Networks. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
