activity
20182026
most citedFedCorr: Multi-Stage Federated Learning for Label Noise Correction

10 citations · 46 across the 23 of their papers we have counts for

collaborators

22 papers

eess.IV2026

Single-Model Adaptive Wireless Image Transmission via Feature Sparsity Regularization

Xianghao Cui, Li Lan, Qi He +4

Learned joint source-channel coding (JSCC) enables robust wireless image transmission by jointly optimizing the transmitter and receiver over differentiable channel models. For ban…

cs.LG2025

Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization

Kun Guo, Xuefei Li, Xijun Wang +3

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices w…

cs.IT2025

Analysis of SINR Coverage in LEO Satellite Networks through Spatial Network Calculus

Yuting Tang, Yufan He, Yi Zhong +3

We introduce a new analytical framework, developed based on the spatial network calculus, for performance assessment of Low Earth Orbit (LEO) satellite networks. Specifically, we m…

cs.IT2025

Towards Secure Semantic Transmission In the Era of GenAI: A Diffusion-based Framework

Boxiang He, Zihan Chen, Junshan Luo +4

Semantic communication, due to its focus on the transmitting meaning rather than the raw bit data, poses unique security challenges compared to the traditional communication system…

cs.IT2025

Diffusion-enabled Secure Semantic Communication Against Eavesdropping

Boxiang He, Zihan Chen, Fanggang Wang +3

In this paper, AN is introduced into semantic communication systems for the first time to prevent semantic eavesdropping. However, the introduction of AN also poses challenges for…

cs.IT2025

Task-oriented Age of Information for Remote Inference with Hybrid Language Models

Shuying Gan, Xijun Wang, Chenyuan Feng +4

Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) through their advanced reasoning capabilities, but their extensive parameter sets introdu…