Towards Hyperparameter Transfer for Differentially Private Optimization
Scaling laws and hyperparameter tuning remain central challenges in training large-scale models, particularly under differential privacy (DP), where optimization is further complicated by noise injection and per-sample gradient clipping. While Maximal Update Parametrization ($μ$P) allows for zero-shot hyperparameter transfer in standard training, we show that it fails under DP constraints due to the distinct spectral properties of high-dimensional noise versus low-rank gradients. In this work, we introduce DP-$μ$P, a theoretically grounded framework that extends $μ$P to the privacy-preserving setting. By analyzing the interaction between gradient clipping, noise injection, and feature lea...
| Authors | Tianze Wang, Zhiqi Bu, Linjun Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 16 October 2026 |
| Last updated at source | 19 October 2026 |
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