Conghao Zhou

FedPKD+: A Prototype-Based Framework for Efficient and Flexible Heterogeneous Federated Learning

Federated Learning (FL) enables collaborative model training across mobile and edge devices without sharing raw data, but its deployment is hindered by <italic>system heterogeneity</italic> and <italic>non-IID</italic> data. Existing FL methods either require homogeneous architectures or suffer from accuracy loss and h...

Service agent for user-centric QoE-aware communication service provision in mobile AR

Frequency-Guided Acceleration of Diffusion-Based Interactive Denoising in Mobile Edge Networks

Artificial Intelligence-Generated Content is reshaping interactive content creation. However, provisioning diffusion-based interactive denoising services in mobile edge networks remains challenging, due to the demanding computation and communication resources. To address these challenges, in this paper, we investigate...

RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization

RadioVIL is proposed, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem and unlocks accurate zero-shot vehicle localization directly from sparse radio maps, paving a robust way for ISAC at the 6G edge.