Deep Diffusion Gradients Leakage in Federated Learning

Abstract

In federated learning (FL), multiple clients train a global model by sharing gradients. Since the client data remains locally, federated learning is considered privacy-safe. Although previous studies have demonstrated the feasibility of recovering client data from shared gradients, most studies assume that there is no privacy defense in the federated setting. In this work, we propose Deep Diffusion Gradients Leakage, which uses the prior information of the diffusion model to compensate for the gradient information degradation caused by privacy defense, and reconstructs high-quality original images from deep neural networks under privacy defense. We also propose a step-by-step optimization strategy to solve the gradient matching problem in the process of reconstructing the image by sequentially optimizing the latent variables of the diffusion model, and more finely control the reconstruction results. We hope that our method can promote the development of privacy-preserving methods for federated learning.