1 citations · 1 across the 4 of their papers we have counts for
4 papers
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
Xingrun Yan, Shiyuan Zuo, Yifeng Lyu +2
Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users…
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
Xingrun Yan, Shiyuan Zuo, Rongfei Fan +4
In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical…
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can…
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), wh…