Title: Federated Learning with Heterogeneous Label Noise: A Dual Structure Approach. Abstract: The performance of federated learning relies heavily on the label quality of each distributed client. In this paper, we consider a federated learning setting with heterogeneous label noise, where each local client might observe training labels with heterogeneous noise rates, which may even drawn from different subsets of the label space. The above high heterogeneity poses challenges for applying the existing label noise learning approaches to each client locally. We formalize the study of federated learning from heterogeneous label noise by firstly identifying two promising label noise generation models. Then, we propose a dual structure approach named FedDual. Intuitively, if there exists a model that filters out the wrongly labeled instances from the local dataset, the effect of label noise can be mitigated. Considering the heterogeneity of local datasets, in addition to the globally shared model, each client in FedDual maintains a local and personalized denoising model. The personalized denoising models can combine information from the global model or other pre-trained models to ensure the performance of denoising. Under this framework, we instantiate our approach with several local sample cleaning methods. We present substantial experiments on MNIST, CIFAR10, and CIFAR100 to demonstrate that FedDual can effectively recognize heterogeneous label noise in different clients and improve the performance of the aggregated model.