On the Optimal Batch Size for Byzantine-Robust Distributed Learning

no code implementations • 23 May 2023 • Yi-Rui Yang, Chang-Wei Shi, Wu-Jun Li

However, for existing BRDL methods, large batch sizes will lead to a drop on model accuracy, even if there is no Byzantine attack.

FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning

no code implementations • 9 Mar 2023 • Yi-Rui Yang, Kun Wang, Wu-Jun Li

Based on ConSpar, we further propose a novel FL framework called FedREP, which is Byzantine-robust, communication-efficient and privacy-preserving.

Federated Learning Privacy Preserving

Buffered Asynchronous SGD for Byzantine Learning

no code implementations • 2 Mar 2020 • Yi-Rui Yang, Wu-Jun Li

In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL.

Edge-computing Federated Learning