no code implementations • 20 Mar 2024 • Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Han Hu, Hangguan Shan, Tony Q. S. Quek
This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity.
no code implementations • 21 Aug 2023 • Shiyuan Zuo, Rongfei Fan, Han Hu, Ning Zhang, Shimin Gong
In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks.
no code implementations • 17 Aug 2023 • Rongfei Fan, Xuming An, Shiyuan Zuo, Han Hu
In case of smooth and strongly convex loss function, we prove our proposed method can achieve minimal training loss at linear rate with any small positive tolerance.
1 code implementation • 17 Aug 2023 • Xuming An, Rongfei Fan, Shiyuan Zuo, Han Hu, Hai Jiang, Ning Zhang
For parameter aggregating in FL, over-the-air computation is a spectrum-efficient solution, which allows all mobile devices to transmit their parameter-mapped signals concurrently to a BS.