Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates

Abstract

In this paper, we propose to mitigate the failures and attacks in federated learning systems from a spatial-temporal perspective. Specifically, we use a clustering-based method to detect and exclude incorrect updates by leveraging their geometric properties in the parameter space. Moreover, to further handle malicious clients with time-varying behaviors, we propose to adaptively adjust the learning rate according to momentum-based update speculation.

Publication
In IEEE International Conference on Parallel and Distributed Systems 2021