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arXiv · 2406.12575

Training Diffusion Models with Federated Learning

Matthijs de Goede·Bart Cox·Jérémie Decouchant
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Abstract

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.

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Matthijs de Goede, Bart Cox, Jérémie Decouchant. 2024-06-18. Training Diffusion Models with Federated Learning. https://arxiv.org/abs/2406.12575

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