Training Diffusion Models with Federated Learning
<|reference_start|>Training Diffusion Models with Federated Learning: The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to their lack of transparency regarding training data. To ad-dress this issue, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training,compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.<|reference_end|>
@article{de goede2024training,
    title={Training Diffusion Models with Federated Learning},
    author={Matthijs de Goede, Bart Cox, J'er'emie Decouchant},
    journal={arXiv preprint arXiv:2406.12575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12575},
    primaryClass={cs.LG cs.DC}
}