Summary of Gi-nas: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Search, by Wenbo Yu et al.
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search
by Wenbo Yu, Hao Fang, Bin Chen, Xiaohang Sui, Chuan Chen, Hao Wu, Shu-Tao Xia, Ke Xu
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 addresses a pressing concern in Federated Learning (FL) systems: the risk of sensitive data being reconstructed through Gradient Inversion Attacks. The authors propose a novel approach, called GI-NAS (Gradient Inversion via Neural Architecture Search), which leverages implicit prior knowledge from over-parameterized neural networks to improve attack performance. Unlike previous methods that rely on explicit prior knowledge or fixed architectures, GI-NAS adaptively searches for the optimal network architecture and captures the underlying priors. Experimental results demonstrate that GI-NAS outperforms state-of-the-art gradient inversion methods in various settings, including high-resolution images, large-sized batches, and advanced defense strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers found a way to protect sensitive data in Federated Learning systems from being hacked. They created a new method called GI-NAS that can better reconstruct data by using information from the neural networks themselves. This is important because previous methods relied too much on special knowledge or fixed structures. The results show that GI-NAS works well even with challenging scenarios like high-resolution images and advanced defenses. |
Keywords
» Artificial intelligence » Federated learning
