48 citations · 144 across the 27 of their papers we have counts for
5 papers · 1 filter
Robust Federated Learning against Model Perturbation in Edge Networks
Dongzi Jin, Yong Xiao, Yingyu Li
Federated Learning (FL) is a promising paradigm for realizing edge intelligence, allowing collaborative learning among distributed edge devices by sharing models instead of raw dat…
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach
Yong Xiao, Rong Xia, Yingyu Li +5
With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unpr…
Adversarial Learning for Implicit Semantic-Aware Communications
Zhimin Lu, Yong Xiao, Zijian Sun +5
Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in…
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
Yong Xiao, Xiaohan Zhang, Guangming Shi +3
Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of ap…
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
Chenxin Xu, Rong Xia, Yong Xiao +3
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are…