Howard Yang - Assistant Professor Communication systems
Date: - Location: Eurecom
Abstract This talk aims to present the current research efforts on the development of implementing distributed machine learning algorithms in wireless systems. Specifically, we provide a comprehensive coverage of a distributed learning paradigm based on over-the-air computing, a.k.a. over-the-air federated learning (OTA-FL). We will present an analytical framework that quantifies the convergence rate of OTA-FL. Then, we discuss the system enhancements from an algorithmic perspective, i.e., adopting adaptive optimizations to accelerate the model training. We also introduce model pruning schemes that reduce the computation and communication overheads for OTA-FL. Finally, we will elaborate on the analysis of generalization error of the statistical models trained by OTA-FL, which shows that wireless interference has the positive potential of improving the generalization capability. Bio Howard H. Yang received the Ph.D. degree in Electrical Engineering from Singapore University of Technology and Design (SUTD), Singapore, in 2017. He was a Postdoctoral Research Fellow at SUTD from 2017 to 2020 and a Visiting Postdoc Researcher at Princeton University from 2018 to 2019. Currently, he is an assistant professor with the ZJU-UIUC Institute, Zhejiang University, China. He is also an adjunct assistant professor with the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, IL, USA. Dr. Yang serves as an editor for the IEEE Transactions on Wireless Communications. He received the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2023, the IEEE Signal Processing Society Best Paper Award in 2022, and the IEEE WCSP 10-Year Anniversary Excellent Paper Award in 2019. He was recognized as the “6G Rising Star” Young Scholar by the Global 6G Conference in 2024.
