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…mehr
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 optimizationtheory, 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.
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 PaperAward 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
Inhaltsangabe
Part 1 Fundamentals and Background.- 1 Introduction.- 2 Fundamentals of Federated Learning.- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning.- 4 Incentive Mechanisms for Federated Learning.- 5 Security and Privacy.- 6 Unsupervised Federated Learning.- Part 3 Federated Learning Applications in Wireless Networks.- 7 Wireless Virtual Reality.- 8 Vehicular Networks and Autonomous Driving Cars.- 9 Smart Industries and Intelligent Reflecting Surfaces.
Part 1 Fundamentals and Background.- 1 Introduction.- 2 Fundamentals of Federated Learning.- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning.- 4 Incentive Mechanisms for Federated Learning.- 5 Security and Privacy.- 6 Unsupervised Federated Learning.- Part 3 Federated Learning Applications in Wireless Networks.- 7 Wireless Virtual Reality.- 8 Vehicular Networks and Autonomous Driving Cars.- 9 Smart Industries and Intelligent Reflecting Surfaces.
