Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models

Matias Mendieta, Guangyu Sun, Chen Chen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2601-2610

Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by reducing communication rounds improving efficiency and providing better security against eavesdropping attacks. Nevertheless data heterogeneity remains a significant challenge impacting performance. This work explores the effectiveness of diffusion models in one-shot FL demonstrating their applicability in addressing data heterogeneity and improving FL performance. Additionally we investigate the utility of our diffusion model approach FedDiff compared to other one-shot FL methods under differential privacy (DP). Furthermore to improve generated sample quality under DP settings we propose a pragmatic Fourier Magnitude Filtering (FMF) method enhancing the effectiveness of the generated data for global model training. Code available at https://github.com/mmendiet/FedDiff.