Summary of Training Diffusion Models with Federated Learning, by Matthijs De Goede et al.
Training Diffusion Models with Federated Learning
by Matthijs de Goede, Bart Cox, Jérémie Decouchant
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a federated diffusion model scheme to address concerns about privacy and data authority in training diffusion-based models for image generation. The current dominance of Big Tech companies in this space raises issues with transparency regarding training data. Our approach, adapting Federated Averaging (FedAvg) to train Denoising Diffusion Models (DDPMs), achieves a significant reduction in parameter exchange while maintaining image quality comparable to centralized training, as evaluated by the FID score. This scheme enables independent and collaborative model training without exposing local data, addressing concerns about privacy, copyright, and data authority. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is trying to solve a problem where big companies have too much control over how images are generated using special computer models. They’re not being transparent about the data they use, which raises concerns about our privacy and who has access to this information. To fix this, the researchers propose a new way of training these models that lets different groups work together without sharing their individual data. This approach is more efficient than previous methods and produces similar results. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation
