Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets

Understand

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.