Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models
Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evaluations. Existing aggregation-based DP-ZO methods reconstruct model updates at a fixed scale, ignoring that the strength of useful signals varies throughout training. Consequently, noise-dominated updates may receive excessive weight and degrade model utility. To address this issue, we propose SAGE, a noise-aware shrinkage method that adaptively attenuates privatized estimates according to their estimated signal quality. SAGE subtracts the known Gaussian noise variance from the observed second moment to estimate the underlying signal energy,...
| Authors | Lele Zheng, Weifeng Kong, Xinyi Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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