Training Diffusion Models with Federated Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916293201887232 |
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| author | de Goede, Matthijs Cox, Bart Decouchant, Jérémie |
| author_facet | de Goede, Matthijs Cox, Bart Decouchant, Jérémie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training Diffusion Models with Federated Learning de Goede, Matthijs Cox, Bart Decouchant, Jérémie Machine Learning Distributed, Parallel, and Cluster Computing I.2.11 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. |
| title | Training Diffusion Models with Federated Learning |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing I.2.11 |
| url | https://arxiv.org/abs/2406.12575 |