Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising

ICLR 2026 Conference Submission19595 Authors

Keywords: Differential Privacy, DP-SGD, Random Matrix Theory, Language Models, Sample Efficiency, Low-Rank Matrix Recovery

Abstract: We address the challenge of sample efficiency in differentially private fine-tuning of large language models (LLMs) using DP-SGD. While DP-SGD provides strong privacy guarantees, the added noise significantly increases the matrix entropy of gradient matrices, disrupting their low-rank structure and slowing optimization. We propose a post-processing algorithm that leverages random matrix theory to denoise gradients, restore low-rank structure, and improve alignment with the original signal. Applied to DP-SGD fine-tuning of RoBERTa on GLUE tasks, our method improves sample efficiency compared to state-of-the-art approaches, substantially reducing training time when optimal performance is not required. This work demonstrates that matrix recovery techniques can enhance the utility of private language model training without compromising privacy guarantees.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 19595