  We present a novel weighted average model based on the mixture of experts
(MoE) concept to provide robustness in Federated learning (FL) against the
poisoned/corrupted/outdated local models. These threats along with the non-IID
nature of data sets can considerably diminish the accuracy of the FL model. Our
proposed MoE-FL setup relies on the trust between users and the server where
the users share a portion of their public data sets with the server. The server
applies a robust aggregation method by solving the optimization problem or the
Softmax method to highlight the outlier cases and to reduce their adverse
effect on the FL process. Our experiments illustrate that MoE-FL outperforms
the performance of the traditional aggregation approach for high rate of
poisoned data from attackers.
