Federated Generative Privacy

Understand

In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting.

• We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial data samples and empirically assess the risk of information disclosure.
• Our experiments show that FedGP is able to generate labelled data of high quality to successfully train and validate supervised models.
• Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.