Private Fine-tuning of Large Language Models with Zeroth-order Optimization

• URL: http://arxiv.org/abs/2401.04343v2
• Date: Mon, 12 Aug 2024 15:07:50 GMT
• Title: Private Fine-tuning of Large Language Models with Zeroth-order Optimization
• Authors: Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal,
• Abstract summary: Differentially private gradient descent (DP-SGD) allows models to be trained in a privacy-preserving manner. We introduce DP-ZO, a private fine-tuning framework for large language models by privatizing zeroth order optimization methods.
• Score: 51.19403058739522
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Differentially private stochastic gradient descent (DP-SGD) allows models to be trained in a privacy-preserving manner, but has proven difficult to scale to the era of foundation models. We introduce DP-ZO, a private fine-tuning framework for large language models by privatizing zeroth order optimization methods. A key insight into the design of our method is that the direction of the gradient in the zeroth-order optimization we use is random and the only information from training data is the step size, i.e., a scalar. Therefore, we only need to privatize the scalar step size, which is memory-efficient. DP-ZO provides a strong privacy-utility trade-off across different tasks, and model sizes that are comparable to DP-SGD in $(\varepsilon,\delta)$-DP. Notably, DP-ZO possesses significant advantages over DP-SGD in memory efficiency, and obtains higher utility in $\varepsilon$-DP when using the Laplace mechanism.

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