Wireless innovations Next-generation
Online Workshop (WiNOW)
3-6 November, 2025 // Virtual
Meixia Tao
Shanghai Jiao Tong University
Meixia Tao is a Distinguished Professor with the Department of Electronic Engineering at Shanghai Jiao Tong University, China. She received the B.S. degree in electronic engineering from Fudan University, Shanghai, China, in 1999, and the Ph.D. degree in electrical and electronic engineering from Hong Kong University of Science and Technology in 2003. Her current research interests include wireless edge learning, semantic communications, integrated communication-computing-sensing, AI-based channel modeling and beamforming. She has published over 300 research papers in peer-reviewed journals and conference. Prof. Tao has received several awards and recognitions, including the 2020 First Prize in Natural Science from the Shanghai Municipality, the 2019 IEEE Marconi Prize Paper Award, and the 2013 IEEE Heinrich Hertz Award for Best Communications Letters. Prof. Tao is currently a Vice-Chair of the Information Theory Society of the Chinese Institute of Electronics. She is a Fellow of IEEE and receives the National Science Fund for Distinguished Young Scholars in China.
Talk Title: Federated Edge Learning for 6G
As a promising distributed learning paradigm, federated edge learning (FEEL) leverages the sensing, communication, and computation capabilities of edge devices in wireless networks to enable collaborative AI model training without sharing raw local data. FEEL not only offers data privacy preservation, reduced communication costs, and enhanced network scalability, but also facilitates the efficient construction of high-accuracy, highly generalizable AI models. This talk will explore the pivotal role of FEEL in advancing both the wireless for AI" andAI for wireless” paradigms, thereby facilitating the realization of scalable, adaptive, and intelligent 6G networks. Specifically, I will first introduce a novel task-oriented communication principle for deploying FEEL in wireless networks. I will then cover the domain-specific optimizations of FEEL for applications in 6G air-interface design and optimization. Finally, I will highlight promising research directions to enhance FEEL’s design and impact in 6G.