Summary of Dp-kan: Differentially Private Kolmogorov-arnold Networks, by Nikita P. Kalinin et al.
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by Nikita P. Kalinin, Simone Bombari, Hossein Zakerinia, Christoph H. Lampert
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 research paper explores the application of the Kolmogorov-Arnold Network (KAN) in differentially private model training. The authors compare the performance of KAN with classical Multilayer Perceptron (MLP) and demonstrate that KAN can be made private using the DP-SGD algorithm. The results show that KAN’s accuracy is comparable to MLP’s, but both models experience similar deterioration due to privacy constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at a new kind of computer model called the Kolmogorov-Arnold Network (KAN). Researchers wanted to see if KAN can be used for something called differentially private model training. They compared KAN with another type of model, Multilayer Perceptron (MLP), and found that KAN works just as well but gets worse when we try to keep it private. |
