  Federated learning achieves joint training of deep models by connecting
decentralized data sources, which can significantly mitigate the risk of
privacy leakage. However, in a more general case, the distributions of labels
among clients are different, called ``label distribution skew''. Directly
applying conventional federated learning without consideration of label
distribution skew issue significantly hurts the performance of the global
model. To this end, we propose a novel federated learning method, named FedMGD,
to alleviate the performance degradation caused by the label distribution skew
issue. It introduces a global Generative Adversarial Network to model the
global data distribution without access to local datasets, so the global model
can be trained using the global information of data distribution without
privacy leakage. The experimental results demonstrate that our proposed method
significantly outperforms the state-of-the-art on several public benchmarks.
Code is available at \url{https://github.com/Sheng-T/FedMGD}.
