Summary of Gradient Inversion Of Federated Diffusion Models, by Jiyue Huang et al.
Gradient Inversion of Federated Diffusion Models
by Jiyue Huang, Chi Hong, Lydia Y. Chen, Stefanie Roos
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper investigates the privacy risks associated with training diffusion models in a federated learning setting. Diffusion models are capable of generating high-quality image data, but they require massive amounts of real data to train effectively. In a federated learning approach, each party shares gradients instead of raw data, which raises concerns about privacy leakage. The authors propose two optimization methods: GIDM and GIDM+. GIDM uses the well-trained generative model as prior knowledge to constrain the inversion search space, while GIDM+ coordinates the optimization of unknown data, noise, and sampling step. The results demonstrate that sharing gradients can lead to reconstructing high-quality images, highlighting the vulnerability of this approach for data protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful tool that can create amazing pictures. This tool needs lots of real pictures to learn how to make its own pictures look great. But what if someone else has some of these pictures and wants to use them without sharing the actual pictures? That’s where this paper comes in. It talks about how we can use these powerful tools, called diffusion models, in a way that protects our privacy. The authors show that even with super high-quality images, there are still ways to make copies of these images if we’re not careful. They propose some new methods to help keep our pictures safe. |
Keywords
» Artificial intelligence » Diffusion » Federated learning » Generative model » Optimization
