Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
<|reference_start|>Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models: 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.<|reference_end|>
@article{mendieta2024navigating,
    title={Navigating Heterogeneity and Privacy in One-Shot Federated Learning with
  Diffusion Models},
    author={Matias Mendieta, Guangyu Sun, Chen Chen},
    journal={arXiv preprint arXiv:2405.01494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01494},
    primaryClass={cs.CV cs.CR cs.LG}
}