Biography: Xianhao Chen is an assistant professor at the Department of Electrical and Electronic Engineering, the University of Hong Kong, where he leads the Wireless Information & Intelligence (WILL) lab. He obtained his Ph.D. degree in electrical and computer engineering from the University of Florida in 2022, and his B.Eng. degree from Southwest Jiaotong University, Chengdu, China, in 2017. He received the 2022 ECE graduate excellence award for research from the University of Florida. He has served as a TPC member of several conferences, such as IEEE GLOBECOM, IEEE IWQoS, and a session chair of IEEE TENCON. He serves as an Associate Editor of ACM Computing Surveys.
Talk Title:
Split Learning for 6G Edge Intelligence
Talk Abstract :
The next-generation mobile network aims to natively support distributed intelligence, such as federated learning, across massive wireless edge devices. Unfortunately, in the era of large models, the deployment of federated learning faces significant obstacles due to the limited resources on edge devices. In this talk, I will briefly introduce split learning (SL) and elucidate how it overcomes resource limitations via device-server co-training, which transforms next-generation edge AI. Then, I will present our recent work on adaptive split federated learning (AdaptSFL) in resource-constrained edge networks. Specifically, our work first provides a unified convergence analysis of split federated learning (SFL) to quantify the impact of model splitting and client-side model aggregation on the learning performance, based on which the AdaptSFL framework is developed to adaptively control model splitting and client-side model aggregation to balance communication-computing latency and training convergence in SFL. Simulations results demonstrate the effectiveness of our approach in accelerating SFL under resource constraints. At last, I will conclude the talk by discussing open problems and challenges in SL at the wireless edge.
