Federated Generative Adversarial Network (FedGAN) is a communication-efficient approach to train a GAN across distributed clients without clients having to share their sensitive training data. In this paper, we experimentally show that FedGAN generates biased data points under non-independent-and-identically-distributed (non-iid) settings. Also, we propose Bias-Free FedGAN, an approach to generate bias-free synthetic datasets using FedGAN. Bias-Free FedGAN has the same communication cost as that of FedGAN. Experimental results on image datasets (MNIST and FashionMNIST) validate our claims.

翻译：在本文中,我们实验性地表明,FedGAN在非独立和身份分配(非二分)设置下产生了偏向性数据点;我们还提议采用Bias-fef-fedGAN,一种利用FedGAN生成无偏向性合成数据集的方法;Bias-free FedGAN拥有与FedGAN相同的通信成本;图像数据集(MNIST和FashonMNIST)的实验结果证实了我们的说法。