Ph.D. researcher, King¢s College London, UK
Title of talk: Edge intelligence for 6G: From Federated Learning to Digital Twins and Large Models
Abstract:The vision of 6G and beyond is to embed intelligence deeply into wireless networks, enabling autonomous, adaptive, and scalable services at the edge. However, the practical deployment of edge intelligence (EI) is hindered by critical challenges, most notably the heterogeneity of data, computation, and communication resources across devices. Under stringent latency and reliability requirements, open questions arise such as how to cope with the dynamic and unpredictable nature of wireless channels, and how to ensure scalability given the increasing complexity of managing distributed learning systems. Meanwhile, the rapid advances in generative AI, digital twins, and large-scale foundation models are creating unprecedented opportunities to overcome these challenges and to reshape the design of next-generation wireless networks.
This talk will cover a range of topics at the intersection of federated learning, generative digital twins, multi-agent reinforcement learning, and large language models for wireless networks. We will discuss how these emerging paradigms can be leveraged to improve scalability, robustness, and explainability of EI, and outline open research questions that point towards building trustworthy and resilient 6G systems
Jingxin Li is a Ph.D. researcher in Telecommunications at King¢s College London. She received her MSc degrees in Artificial Intelligence from the University of Edinburgh and in Communication and Signal Processing from Imperial College London, after completing her B.Eng. in Electrical and Electronic Engineering at the University of Nottingham. Her research focuses on federated learning, reinforcement learning, wireless digital twins, and AI/ML-driven optimization for 5G/6G mobile networks and edge computing. She has published in leading IEEE journals and conferences such as IEEE Internet of Things Journal, IEEE Open Journal of the Communications Society, and IEEE ICC, where her work received the Best Student Paper Award at IEEE PIMRC 2023. She also serves as a reviewer for top-tier venues including IEEE Internet of Things Journal, IEEE Network, and IEEE Wireless Communications Letters.