Abstract: Efficient allocation of limited resources to competing demands is a crucial issue in the design and management of communication networks. In this presentation, the speaker will first introduce a new reinforcement-learning technique for achieving optimal resource allocation in networks with periodic traffic patterns. The effectiveness of this method will be demonstrated through numerical examples. In addition, the speaker will discuss new approaches for supporting federated learning (FL) and enhancing the learning process through resource adaptation, model pruning, and split learning to address resource constraints. FL involves learning model parameters from distributed data and adapting the learning according to the limited availability of communication, CPU, and GPU resources. The key idea of model pruning is to reduce computation and communication burden by removing unimportant model parameters, while split learning divides the model and learning tasks between the server and user sides. Experimental results demonstrate that these approaches perform close to the optimum or offer significant performance improvements when compared to other methods.

Biography: Kin K. Leung received his B.S. degree from the Chinese University of Hong Kong, and the M.S. and Ph.D. degrees from University of California, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then, he has been the Tanaka Chair Professor at Imperial College in London. He was the Head of Communications and Signal Processing Group from 2019 to 2024 and now serves as Co-Director of the School of Convergence Science in Space, Security and Telecommunications at Imperial. His current research focuses on optimization and machine learning for design and control of large-scale communications, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks.
He is a Fellow of the Royal Academy of Engineering, IEEE Fellow, IET Fellow, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994) and the Royal Society Wolfson Research Merits Award (2004-09). Jointly with his collaborators, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021), the IEEE ComSoc Best Survey Paper Award (2022), the U.S.–UK Science and Technology Stocktake Award (2021), the Lanchester Prize Honorable Mention Award (1997), and several best conference paper awards. He chaired the IEEE Fellow Evaluation Committee for ComSoc (2012-15) and served as the General Chair of the IEEE INFOCOM 2025. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks.