Federated Learning for Wireless Networks

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.

最近，機器學習方案作為下一代無線系統的關鍵推動者，引起了相當大的關注。目前，無線系統主要使用基於集中式訓練和推論過程的機器學習方案，通過將終端設備的數據遷移到第三方集中位置來實現。然而，這些方案會導致終端設備的隱私洩露。為了解決這些問題，可以在網絡邊緣使用分散式機器學習。在這種情況下，聯邦學習（FL）是最重要的分散式學習算法之一，允許設備在保留本地數據的同時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通過無線鏈路在無線設備和邊緣服務器之間進行通信。因此，無線損壞，如無線信道狀態的不確定性，干擾和噪聲對FL的性能有顯著影響。另一方面，聯邦強化學習利用分散的計算能力和數據來解決各種用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無條件參與任務，但這在現實中由於模型訓練成本的原因是不切實際的。因此，建立激勵機制對於FL網絡是不可或缺的。

本書全面介紹了無線網絡中的FL。它分為三個主要部分：第一部分簡要討論了無線網絡中FL的基礎知識，而第二部分全面探討了無線FL的設計和分析，包括資源優化、激勵機制、安全和隱私。它還提出了基於優化理論、圖論和博弈論的幾種解決方案，以優化無線網絡中聯邦學習的性能。最後，第三部分描述了無線網絡中FL的幾個應用。

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

Choong Seon Hong目前是慶熙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的高級會員，也是ACM、IEICE、IPSJ、KIISE、KICS、KIPS和OSIA的會員。他曾擔任國際會議NOMS、IM、APNOMS、E2EMON、CCNC、ADSN、ICPP、DIM、WISA、BcN、TINA、SAINT和ICOIN的總主席、技術篩選委員會主席/成員或組織委員會成員。此外，他曾擔任《通信與網絡學報》、《IEEE網絡與服務管理交易》的副編輯，以及《IEEE通信雜誌》的副技術編輯。他目前是《國際網絡管理期刊》和《未來互聯網》的副編輯。

Latif U. Khan目前在韓國慶熙大學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在邊緣計算和端到端網絡切片中的應用。他還在韓國著名的Brain Korea 21st Century Plus和科學與信息通信部共同資助的項目下，作為智能網絡實驗室的領先研究員工作。在加入慶熙大學之前，他曾在巴基斯坦的UET擔任教職和研究助理。他的研究成果已在高度聲譽的會議和期刊上發表。

Mingzhe Chen目前是普林斯頓大學電氣工程系和中國香港大學深圳研究院的博士後研究員。2016年至2019年期間，他在維吉尼亞理工大學電氣和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。他是IEEE國際通信大會（ICC）2020年最佳論文獎的獲獎者。他在2018年被評為IEEE無線通信交易和2018年和2019年IEEE通信交易的優秀評審。

Dawei Chen目前在美國休斯頓大學電氣和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。

Walid Saad目前是維吉尼亞理工大學電氣和計算機工程系的教授，並領導著網絡科學、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、物聯網和網絡科學。他是IEEE的院士和傑出講師。他曾獲得2013年NSF CAREER獎、2014年AFOSR暑期教職獎和2015年美國海軍研究局（ONR）的青年研究員獎。他是WiOpt（2009年）、ICIMP（2010年）、IEEE WCNC（2012年）、IEEE PIMRC（2015年）、IEEE SmartGridComm（2015年）、EuCNC（2017年）、IEEE GLOBECOM（2018年）和IFIP NTMS（2019年）等八個會議最佳論文獎的作者或合著者。他還獲得了IEEE通信學會2015年Fred W. Ellersick獎、2017年IEEE ComSoc最佳年輕學術界專業人士獎、2018年IEEE ComSoc無線通信委員會早期成就獎和2019年IEEE ComSoc通信理論技術委員會獎。2015年至2017年，他被任命為維吉尼亞理工大學Stephen O. Lane初級教職員，並於2017年被任命為工程學院教職員。他於2019年獲得維吉尼亞理工大學的研究卓越獎。他目前擔任IEEE無線通信交易和IEEE通信交易的編輯。