Communication Efficient Federated Learning for Wireless Networks
暫譯: 無線網路的通訊效率聯邦學習

Chen, Mingzhe, Cui, Shuguang

Communication Efficient Federated Learning for Wireless Networks
  • 出版商: Springer
  • 出版日期: 2024-02-20
  • 售價: $6,800
  • 貴賓價: 9.5 折 $6,460
  • 語言: 英文
  • 頁數: 181
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031512650
  • ISBN-13: 9783031512650
  • 相關分類: Wireless-networks

    • 海外代購書籍(需單獨結帳)

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商品描述

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 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.


商品描述(中文翻譯)

這本書提供了有關無線網路上聯邦學習（Federated Learning, FL）的全面研究。它由三個主要部分組成：（a）FL的基本原理和前提，（b）無線網路上FL的分析與優化，以及（c）無線FL在物聯網系統中的應用。特別是在第一部分，作者詳細概述了廣泛研究的FL框架。在本書的第二部分，作者全面討論了三個關鍵的無線技術，包括無線資源管理、量化和空中計算，以支持在現實無線網路上部署FL。它還提出了幾個基於優化理論、圖論和機器學習的解決方案，以優化無線網路上FL的性能。在本書的第三部分，作者介紹了無線FL算法在自動駕駛車輛控制和移動邊緣計算優化中的應用。

機器學習和數據驅動的方法最近受到相當大的關注，成為下一代智能網路的關鍵推動力。目前，大多數現有的無線網路學習解決方案依賴於通過將邊緣設備生成的數據上傳到數據中心來集中訓練和推斷過程。然而，這種集中式範式可能導致隱私洩漏，違反移動應用的延遲限制，或因邊緣設備的帶寬或功率限制而無法實現。為了解決這些問題，在網路邊緣分散機器學習提供了一個有前景的解決方案，其中邊緣設備使用實時生成的移動數據協作訓練共享模型。避免將數據上傳到中央伺服器不僅有助於保護隱私，還減少了網路流量擁堵和通信成本。聯邦學習（FL）是最重要的分散學習算法之一。特別是，FL使設備能夠在保持數據本地的情況下訓練共享的機器學習模型。然而，在FL中，訓練機器學習模型需要無線設備與邊緣伺服器之間通過無線鏈路進行通信。因此，無線干擾如噪聲、干擾和無線通道狀態的不確定性將顯著影響FL的訓練過程和性能。例如，傳輸延遲可能會顯著影響FL算法的收斂時間。因此，優化無線網路性能對於實施FL算法是必要的。

這本書的目標讀者是計算機科學和電機工程領域的研究人員和高級學生。從事信號處理和機器學習的專業人士也會購買這本書。

作者簡介

Mingzhe Chen (S'15-M'19) is currently an Assistant Professor with the Department of Electrical and Computer Engineering and Institute of Data Science and Computing at University of Miami. His research interests include federated learning, reinforcement learning, virtual reality, unmanned aerial vehicles, and Internet of Things. He has received four IEEE Communication Society journal paper awards including the IEEE Marconi Prize Paper Award in Wireless Communications in 2023, the Young Author Best Paper Award in 2021 and 2023, and the Fred W. Ellersick Prize Award in 2022, and four conference best paper awards at ICCCN in 2023, IEEE WCNC in 2021, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He currently serves as an Associate Editor of IEEE Transactions on Mobile Computing, IEEE Wireless Communications Letters, IEEE Transactions on Green Communications and Networking, and IEEE Transactions on Machine Learning in Communications and Networking.Shuguang Cui (S'99-M'05-SM'12-F'14) received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. He has also served as the Executive Dean for the School of Science and Engineering at CUHK, Shenzhen, the Executive Vice Director at Shenzhen Research Institute of Big Data, and the Director for Future Network of Intelligence Institute (FNii). His current research interests focus on the merging between AI and communication neworks. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds' Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He has served as the general co-chair and TPC co-chairs for many IEEE conferences. He has also been serving as the area editor for IEEE Signal Processing Magazine, and associate editors for IEEE Transactions on Big Data, IEEE Transactions on Signal Processing, IEEE JSAC Series on Green Communications and Networking, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009 2014) and the elected Chair for IEEE ComSoc Wireless Technical Committee (2017 2018). He is a member of the Steering Committee for IEEE Transactions on Big Data and the Chair of the Steering Committee for IEEE Transactions on Cognitive Communications and Networking. He is also the Vice Chair of the IEEE VT Fellow Evaluation Committee and a member of the IEEE ComSoc Award Committee. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. In 2020, he won the IEEE ICC best paper award, ICIP best paper finalist, the IEEE Globecom best paper award. In 2021, he won the IEEE WCNC best paper award. In 2023, he won the IEEE Marconi Best Paper Award, got elected as a Fellow of both Canadian Academy of Engineering and the Royal Society of Canada, and starts to serve as the Editor-in-Chief for IEEE Transactions on Mobile Computing.

作者簡介(中文翻譯)

陳名哲 (S'15-M'19) 目前是邁阿密大學電機與計算機工程系及數據科學與計算研究所的助理教授。他的研究興趣包括聯邦學習、強化學習、虛擬實境、無人機以及物聯網。他曾獲得四項 IEEE 通訊學會期刊論文獎，包括 2023 年的 IEEE Marconi Prize Paper Award in Wireless Communications、2021 年及 2023 年的 Young Author Best Paper Award，以及 2022 年的 Fred W. Ellersick Prize Award，並在 2023 年的 ICCCN、2021 年的 IEEE WCNC、2020 年的 IEEE ICC 和 2020 年的 IEEE GLOBECOM 獲得四項會議最佳論文獎。他目前擔任 IEEE Transactions on Mobile Computing、IEEE Wireless Communications Letters、IEEE Transactions on Green Communications and Networking 及 IEEE Transactions on Machine Learning in Communications and Networking 的副編輯。

崔曙光 (S'99-M'05-SM'12-F'14) 於 2005 年在美國加州史丹佛大學獲得電機工程博士學位。此後，他在亞利桑那大學、德州農工大學、加州大學戴維斯分校及香港中文大學（深圳）擔任助理教授、副教授、正教授及講座教授。他還曾擔任香港中文大學（深圳）科學與工程學院的執行院長、深圳大數據研究院的執行副主任，以及未來智能網絡研究所 (FNii) 的主任。他目前的研究興趣集中在人工智慧與通訊網絡的融合上。他於 2014 年被選為湯森路透高被引研究者，並被 ScienceWatch 列入全球最具影響力的科學思想家名單。他曾獲得 IEEE 信號處理學會 2012 年最佳論文獎。他擔任過多個 IEEE 會議的總共同主席及技術程序委員會共同主席。他還擔任 IEEE Signal Processing Magazine 的區域編輯，以及 IEEE Transactions on Big Data、IEEE Transactions on Signal Processing、IEEE JSAC 系列綠色通訊與網絡及 IEEE Transactions on Wireless Communications 的副編輯。他曾當選 IEEE 信號處理學會 SPCOM 技術委員會成員 (2009-2014) 及 IEEE ComSoc 無線技術委員會主席 (2017-2018)。他是 IEEE Transactions on Big Data 的指導委員會成員，並擔任 IEEE Transactions on Cognitive Communications and Networking 的指導委員會主席。他還是 IEEE VT Fellow 評估委員會的副主席及 IEEE ComSoc 獎勵委員會的成員。他於 2013 年當選為 IEEE Fellow，2014 年成為 IEEE ComSoc 傑出講者，2019 年成為 IEEE VT Society 傑出講者。2020 年，他獲得 IEEE ICC 最佳論文獎、ICIP 最佳論文決賽入圍者及 IEEE Globecom 最佳論文獎。2021 年，他獲得 IEEE WCNC 最佳論文獎。2023 年，他獲得 IEEE Marconi 最佳論文獎，當選為加拿大工程學院及加拿大皇家學會的院士，並開始擔任 IEEE Transactions on Mobile Computing 的主編。

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