Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning

Abstract

Federated Learning (FL) is a decentralized model training framework that aims to merge isolated data islands while maintaining data privacy. However, recent studies have revealed that Generative Adversarial Network (GAN)-based attacks can be employed in FL to learn the distribution of private datasets and reconstruct recognizable images. In this article, we exploit defenses against GAN-based attacks in FL and propose a framework, Anti-GAN, to prevent attackers from learning the real distribution of the victim's data. The core idea of Anti-GAN is to manipulate the visual features of private training images to make them indistinguishable to human eyes even restored by attackers. Specifically, Anti-GAN projects the private dataset onto a GAN's generator and combines the generated fake images with the actual images to create the training dataset, which is then used for federated model training. The experimental results demonstrate that Anti-GAN is effective in preventing attackers from learning the distribution of private images while causing minimal harm to the accuracy of the federated model.