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Titlebook: Communication Efficient Federated Learning for Wireless Networks; Mingzhe Chen,Shuguang Cui Book 2024 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 17:01:16 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Communication Efficient Federated Learning for Wireless Networks
編輯Mingzhe Chen,Shuguang Cui
視頻videohttp://file.papertrans.cn/231/230395/230395.mp4
概述Offers a comprehensive and systematic book on design of federated learning.Provides key approaches for optimizing performance of federated learning.Demonstrates effective applications of federated lea
叢書名稱Wireless Networks
圖書封面Titlebook: Communication Efficient Federated Learning for Wireless Networks;  Mingzhe Chen,Shuguang Cui Book 2024 The Editor(s) (if applicable) and Th
描述.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 centralizin
出版日期Book 2024
關鍵詞Distributed learning; Federated learning; Resource Allocation; Quantization; Over the air computation; Au
版次1
doihttps://doi.org/10.1007/978-3-031-51266-7
isbn_softcover978-3-031-51268-1
isbn_ebook978-3-031-51266-7Series ISSN 2366-1186 Series E-ISSN 2366-1445
issn_series 2366-1186
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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沙發(fā)
發(fā)表于 2025-3-21 22:29:02 | 只看該作者
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Introduction,f the new . command for typesetting the text of the online abstracts (cf. source file of this chapter template .) and include them with the source files of your manuscript. Use the plain . command if the abstract is also to appear in the printed version of the book.
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Federated Learning for Autonomous Vehicles Control,trollers or traditional learning-based controllers, solely trained by each CAV’s local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics.
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發(fā)表于 2025-3-22 14:24:17 | 只看該作者
https://doi.org/10.1007/978-3-030-25482-7ts, we introduce several optimization theory based methods for resource management aiming to optimize the wireless FL performance metrics. Finally, several simulations are implemented to demonstrate the performance of the designed FL.
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978-3-031-51268-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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