Professor of Electrical and Computer Engineering
Seminar: Distributed Machine Learning in Wireless Networks: Challenges and Opportunities
Abstract: Due to major communication, privacy, and scalability challenges stemming from the emergence of large-scale Internet of Things services, machine learning is witnessing a major departure from traditional centralized cloud architectures toward a distributed machine learning (ML) paradigm where data is dispersed and processed across multiple edge devices. A prime example of this emerging distributed ML paradigm is Google's renowned federated learning framework. Despite the tremendous recent interest in distributed ML, remarkably, prior work in the area remains largely focused on the development of distributed ML algorithms for inference and classification tasks. In contrast, in this talk, we focus on two novel distributed ML perspectives. We first investigate how, when deployed over real-world wireless networks, the performance of distributed ML (particularly federated learning) will be affected by inherent network properties such as bandwidth limitations and delay. We then make the case for the necessity of a novel, joint learning and communication design perspective when deploying federated learning over practical wireless networks such as cellular systems. Then, we turn our attention towards the design of new distributed ML algorithms that can be used for generative tasks. In this context, we introduce the novel framework of brainstorming generative adversarial networks (BGANs) that constitutes one of the first implementations of distributed, multi-agent GAN models. We show how BGAN allows multiple agents to gain information from one another without sharing their real datasets but by "brainstorming" their generated data samples. We then demonstrate the higher accuracy and scalability of BGAN compared to the state of the art. We also illustrate how BGAN can be used for analyzing a millimeter wave channel estimation problem in wireless networks that rely on unmanned aerial vehicles (UAVs). We conclude this talk with an overview on the future outlook of the exciting area of distributed ML.
About the speaker: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at 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. Dr. Saad is a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is also the 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 in 2015. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society; the 2017 IEEE ComSoc Best Young Professional in Academia award; and the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award. 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, IEEE Transactions on Mobile Computing, IEEE Transactions on Cognitive Communications and Networking, and IEEE Transactions on Information Forensics and Security. He is an Editor-at-Large for the IEEE Transactions on Communications.
Host: Nikos Sidiropoulos, Louis T. Rader Professor and Chair, Electrical and Computer Engineering