  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. Our approach generates metadata at the aggregator using the
models received from clients and retrains the federated model to achieve
bias-free results for image synthesis. Bias-Free FedGAN has the same
communication cost as that of FedGAN. Experimental results on image datasets
(MNIST and FashionMNIST) validate our claims.
