1 citations · 2 across the 9 of their papers we have counts for
8 papers · 1 filter
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approac…
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Shiyuan Zuo, Rongfei Fan, Cheng Zhan +3
Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of…
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…
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient
Rongfei Fan, Xuming An, Shiyuan Zuo +1
Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss funct…
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap
Shiyuan Zuo, Rongfei Fan, Han Hu +2
In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks. At each user, model parameter is firstly…
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning
Xuming An, Rongfei Fan, Shiyuan Zuo +3
Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the trai…