Title: Federated Learning Nodes Can Reconstruct Peers’ Image Data
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Federated Learning, Gradient Inversion, Data Reconstruction

Abstract: Federated learning (FL) is a privacy-preserving machine learning framework that enables multiple nodes to train models on their local data and periodically average weight updates to benefit from other nodes' training. Each node's goal is to collaborate with other nodes to improve the model's performance while keeping its training data private. However, this framework does not guarantee data privacy. Prior work has shown that the gradient-sharing steps in FL can be vulnerable to data reconstruction attacks from a honest-but-curious central server. In this work, we show that a honest-but-curious node/client can also launch attacks to reconstruct peers' image data through gradient inversion in a centralized system, presenting a severe privacy risk. We demonstrate that a single client can silently reconstruct other clients' private images using diluted information available within consecutive updates. We leverage state-of-the-art diffusion models to enhance the perceptual quality and recognizability of the reconstructed images, further demonstrating the risk of information leakage at a semantic level. This highlights the need for more robust privacy-preserving mechanisms that protect against silent client-side attacks during federated training. Our source code and pretrained model weights are available at https://anonymous.4open.science/r/curiousclient-5B6F.