Summary of Dr-encoder: Encode Low-rank Gradients with Random Prior For Large Language Models Differentially Privately, by Huiwen Wu et al.
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
by Huiwen Wu, Deyi Zhang, Xiaohan Li, Xiaogang Xu, Jiafei Wu, Zhe Liu
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper explores ways to ensure end-to-end privacy in fine-tuning large language models (LLMs), which are capable of tasks such as understanding languages, translation, and solving partial differential equations. The transformer architecture is a key component in building foundation models. By investigating three potential information leakage scenarios during federated fine-tuning, the authors propose an innovative solution that incorporates two-stage randomness to guarantee privacy. This involves training a gradient auto-encoder with Gaussian noise prior to gradient statistics and then fine-tuning the LLM with differential privacy guarantees. The proposed method demonstrates improved efficiency and accuracy on various foundation models and benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that large language models are private when they’re being trained in many different places at the same time. These models can do lots of cool things like understand languages, translate text, and even solve math problems. The problem is that if someone gets their hands on these models, they could learn a lot about what we’re doing. To fix this, the authors came up with a clever way to add noise to the model’s updates so it can’t be traced back to individual users. They tested their method and found that it works well. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Transformer » Translation
