Federated Learning for Wireless Networks
暫譯: 無線網路的聯邦學習

Hong, Choong Seon, Khan, Latif U., Chen, Mingzhe

Federated Learning for Wireless Networks
  • 出版商: Springer
  • 出版日期: 2022-12-03
  • 售價: $7,100
  • 貴賓價: 9.5 折 $6,745
  • 語言: 英文
  • 頁數: 253
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811649650
  • ISBN-13: 9789811649653
  • 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

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 train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks.
 

This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

商品描述(中文翻譯)

最近，機器學習方案因為成為下一代無線系統的關鍵推動力而受到廣泛關注。目前，無線系統大多使用基於集中訓練和推斷過程的機器學習方案，這些方案將終端設備的數據遷移到第三方集中位置。然而，這些方案導致終端設備的隱私洩漏。為了解決這些問題，可以在網絡邊緣使用分散式機器學習。在這個背景下，聯邦學習（Federated Learning, FL）是最重要的分散式學習算法之一，允許設備在保持數據本地的情況下訓練共享的機器學習模型。然而，在無線網絡中應用FL並優化其性能涉及一系列研究主題。例如，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，無線通道狀態的不確定性、干擾和噪聲等無線損害會顯著影響FL的性能。另一方面，聯邦強化學習（Federated Reinforcement Learning）利用分散的計算能力和數據來解決在各種使用案例中出現的複雜優化問題，例如干擾對齊、資源管理、聚類和網絡控制。傳統上，FL假設邊緣設備在被邀請時會無條件參與任務，但由於模型訓練的成本，這在現實中並不實際。因此，建立激勵機制對於FL網絡是不可或缺的。

本書提供了無線網絡中FL的全面概述。它分為三個主要部分：第一部分簡要討論了無線網絡中FL的基本原理，第二部分全面檢視了無線FL的設計和分析，涵蓋資源優化、激勵機制、安全性和隱私。它還基於優化理論、圖論和博弈論提出了幾個解決方案，以優化無線網絡中聯邦學習的性能。最後，第三部分描述了FL在無線網絡中的幾個應用。

作者簡介

Choong Seon Hong is currently a Professor with the Department of Computer Science and Engineering, Kyung Hee University. His research interests include the AI networking, machine learning, edge computing. He is senior member of IEEE, and a member of ACM, IEICE, IPSJ, KIISE, KICS, KIPS, and OSIA. He has served as the General Chair, a TPC Chair/Member, or an Organizing Committee Member for international conferences such as NOMS, IM, APNOMS, E2EMON, CCNC, ADSN, ICPP, DIM, WISA, BcN, TINA, SAINT, and ICOIN. In addition, he was an Associate Editor of the Journal of Communications and Networks, IEEE Transactions on Networks and Service Management and an Associate Technical Editor of the IEEE Communications Magazine. He is currently an associate editor of the International Journal of Network Management, and Future Internet.

Latif U. Khan is currently pursuing the Ph.D. degree in computer engineering with Kyung Hee University (KHU), South Korea. His research interests include analytical techniques of optimization and game theory to edge computing, and end-to-end network slicing. He is also working as a Leading Researcher with the Intelligent Networking Laboratory under a project jointly funded by the prestigious Brain Korea 21st Century Plus and Ministry of Science and ICT, South Korea. Prior to joining the KHU, he has served as a Faculty Member and a Research Associate with UET, Peshawar, Pakistan. He has published his works in highly reputable conferences and journals.

Mingzhe Chen is currently a Post-Doctoral Researcher at the Electrical Engineering Department, Princeton University and at the Chinese University of Hong Kong, Shenzhen, China. From 2016 to 2019, he was a Visiting Researcher at the Department of Electrical and Computer Engineering, Virginia Tech. His research interests include federated learning, reinforcement learning, virtual reality, unmanned aerial vehicles, and wireless networks. He was a recipient of the IEEE International Conference on Communications (ICC) 2020 Best Paper Award. He was an exemplary reviewer for IEEE Transactions on Wireless Communications in 2018 and IEEE Transactions on Communications in 2018 and 2019.

Dawei Chen is currently pursuing the Ph.D. degree with the Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA. His research interests include machine learning, edge/cloud computing, and wireless networks.

Walid Saad is currently a Professor with the Department of Electrical and Computer Engineering, Virginia Tech, where he leads the Network Science, Wireless, and Security (NEWS) Laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. He is an IEEE fellow and IEEE Distinguished Lecturer. He was a recipient of the NSF CAREER Award in 2013, the AFOSR Summer Faculty Fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author or coauthor of eight conference best paper awards such as WiOpt in 2009, ICIMP in 2010, the IEEE WCNC in 2012, the IEEE PIMRC in 2015, the IEEE SmartGridComm in 2015, EuCNC in 2017, the IEEE GLOBECOM in 2018, and IFIP NTMS in 2019. He was also the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2017 IEEE ComSoc Best Young Professional in Academia Award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee. From 2015 to 2017, he was named as the Stephen O. Lane Junior Faculty Fellow at Virginia Tech, and he was named as the College of Engineering Faculty Fellow in 2017. He received the Dean's Award for Research Excellence from Virginia Tech in 2019. He currently serves as an Editor for the IEEE Transactions on Wireless Communications, the IEEE Transactions on Mobile Computing, and the IEEE Transactions on Cognitive Communications and Networking. He is an Editor-at-Large for the IEEE Transactions on Communications.

Zhu Han is currently a John and Rebecca Moores Professor with the Electrical and Computer Engineering Department, University of Houston, TX, USA, and also with in the Computer Science Department, University of Houston. He is also a Chair Professor with National Chiao Tung University. His research interests include wireless resource allocation and management, wireless communications and networking, game theory, big data analysis, security, and smart grid. He has been an AAAS Fellow since 2019 and has also been an ACM Distinguished Member since 2019. He received an NSF Career Award, in 2010, the Fred W. Ellersick Prize of the IEEE Communication Society, in 2011, the EURASIP Best Paper Award for the Journal on Advances in Signal Processing, in 2015, the IEEE Leonard G. Abraham Prize in the field of communications systems (Best Paper Award in IEEE JSAC), in 2016, and several best paper awards in IEEE conferences. He was an IEEE Communications Society Distinguished Lecturer from 2015 to 2018. He has been a 1% Highly Cited Researcher since 2017 according to Web of Science. He is now an IEEE fellow

 

作者簡介(中文翻譯)

洪忠璇目前是京畿大學計算機科學與工程系的教授。他的研究興趣包括人工智慧網絡、機器學習和邊緣計算。他是IEEE的資深會員，並且是ACM、IEICE、IPSJ、KIISE、KICS、KIPS和OSIA的會員。他曾擔任NOMS、IM、APNOMS、E2EMON、CCNC、ADSN、ICPP、DIM、WISA、BcN、TINA、SAINT和ICOIN等國際會議的總主席、技術程序委員會主席/成員或組織委員會成員。此外，他曾擔任《通訊與網絡期刊》的副編輯、《IEEE網絡與服務管理期刊》的副編輯，以及《IEEE通訊雜誌》的副技術編輯。目前，他是《國際網絡管理期刊》和《未來互聯網》的副編輯。

拉提夫·U·汗目前在京畿大學（KHU）攻讀計算機工程博士學位。他的研究興趣包括優化和博弈論的分析技術在邊緣計算和端到端網絡切片中的應用。他還擔任智能網絡實驗室的首席研究員，該項目由著名的韓國21世紀大腦計畫和科學技術部共同資助。在加入KHU之前，他曾在巴基斯坦白沙瓦的UET擔任教職和研究助理。他的研究成果已發表在多個高聲望的會議和期刊上。

陳名哲目前是普林斯頓大學電機工程系和中國香港中文大學（深圳）的博士後研究員。從2016年到2019年，他在維吉尼亞理工大學的電氣與計算機工程系擔任訪問研究員。他的研究興趣包括聯邦學習、強化學習、虛擬現實、無人機和無線網絡。他曾獲得2020年IEEE國際通訊會議（ICC）最佳論文獎。他在2018年和2019年擔任IEEE無線通訊期刊和IEEE通訊期刊的優秀審稿人。

陳大偉目前在美國德克薩斯州休斯頓的休斯頓大學電氣與計算機工程系攻讀博士學位。他的研究興趣包括機器學習、邊緣/雲計算和無線網絡。

瓦利德·薩阿德目前是維吉尼亞理工大學電氣與計算機工程系的教授，並領導網絡科學、無線和安全（NEWS）實驗室。他的研究興趣包括無線網絡、機器學習、博弈論、安全、無人機、網絡物理系統和網絡科學。他是IEEE的會士和IEEE傑出講者。他曾於2013年獲得NSF CAREER獎，2014年獲得AFOSR夏季教職員獎學金，2015年獲得海軍研究辦公室（ONR）的青年研究者獎。他是2009年WiOpt、2010年ICIMP、2012年IEEE WCNC、2015年IEEE PIMRC、2015年IEEE SmartGridComm、2017年EuCNC、2018年IEEE GLOBECOM和2019年IFIP NTMS等八個會議最佳論文獎的作者或合著者。他還於2015年獲得IEEE通訊學會的Fred W. Ellersick獎，2017年獲得IEEE ComSoc最佳青年專業學者獎，2018年獲得IEEE ComSoc無線通訊委員會早期成就獎，以及2019年IEEE ComSoc通訊理論技術委員會的獎項。從2015年到2017年，他被評為維吉尼亞理工大學的Stephen O. Lane青年教職員獎學者，並於2017年被評為工程學院教職員獎學者。他於2019年獲得維吉尼亞理工大學的院長研究卓越獎。目前，他擔任《IEEE無線通訊期刊》、《IEEE移動計算期刊》和《IEEE認知通訊與網絡期刊》的編輯，並擔任《IEEE通訊期刊》的特約編輯。

朱漢目前是美國德克薩斯州休斯頓大學電氣與計算機工程系的John and Rebecca Moores教授，同時也在休斯頓大學計算機科學系任教。他還是國立交通大學的講座教授。他的研究興趣包括無線資源分配與管理、無線通訊與網絡、博弈論、大數據分析、安全和智能電網。他自2019年以來一直是美國科學促進會（AAAS）的會士，自2019年以來也是ACM的傑出會員。他於2010年獲得NSF Career獎，2011年獲得IEEE通訊學會的Fred W. Ellersick獎，2015年獲得EURASIP信號處理進展期刊最佳論文獎，2016年獲得IEEE通訊系統領域的Leonard G. Abraham獎（IEEE JSAC最佳論文獎），以及多個IEEE會議的最佳論文獎。他於2015年至2018年擔任IEEE通訊學會的傑出講者。根據Web of Science，他自2017年以來一直是1%的高被引研究者。他目前是IEEE的會士。

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