Federated Learning Over Wireless Edge Networks (Hardcover, 1st ed. 2022)

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Wireless Networks
Release date:	October 2022
First published:	2022
Authors:	Wei Yang Bryan Lim • Jer Shyuan Ng • Zehui Xiong • Dusit Niyato • Chunyan Miao
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	165
Edition:	1st ed. 2022
ISBN-13:	978-3-03-107837-8
Categories:	Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > WAP (wireless) technology Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-107837-3
Barcode:	9783031078378

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