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.

我们提出了一种联合扩散模型方案，可以在不暴露本地数据的情况下独立和协作地训练扩散模型。通过对底层 UNet 骨干网的新颖利用，我们实现了在训练过程中参数交换数量的显著降低，最多可达 74％，而同时仍能保持与中心化设置相当的图像质量，依据 FID 分数评估。