Robust Federated Learning with Noisy and Heterogeneous Clients

Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding par-ticipant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication be-tween heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively as-signs corresponding weights to each client in the collabo-rative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative ef-fects of different noise rates/types under both model ho-mogeneous and heterogeneous federated learning settings, consistently outperforming existing methods.