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

Understand

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 paper, 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.