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arXiv · 2103.09876

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

Vaikkunth Mugunthan·Vignesh Gokul·Lalana Kagal·Shlomo Dubnov
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Abstract

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.

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Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal, Shlomo Dubnov. 2021-03-17. Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets. https://arxiv.org/abs/2103.09876

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