  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 generated data for global model training.
