45 citations · 56 across the 20 of their papers we have counts for
4 papers · 1 filter
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…