# DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning

Source: [DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning](<https://www.sota2.com/research/paper/692689b9d6b0dd33429305ca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-02T05:03:10.296Z

Authors: Mihaela Hudi\\c{s}teanu, Nikita P. Kalinin, Edwige Cyffers

Published: 2025-11-25T17:17:48.000Z

## Abstract

Adaptive optimizers are the de facto standard in non-private training as they often enable faster convergence and improved performance. In contrast, differentially private (DP) training is still predominantly performed with DP-SGD, typically requiring extensive compute and hyperparameter tuning. We propose DP-MicroAdam, a memory-efficient and sparsity-aware adaptive DP optimizer. We prove that DP-MicroAdam converges in stochastic non-convex optimization at the optimal $\\mathcal{O}(1/\\sqrt{T})$ rate, up to privacy-dependent constants. Empirically, DP-MicroAdam outperforms existing adaptive DP optimizers and achieves competitive or superior accuracy compared to DP-SGD across a range of benchmarks, including CIFAR-10, large-scale ImageNet training, and private fine-tuning of pretrained transformers. These results demonstrate that adaptive optimization can improve both performance and stability under differential privacy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.20509>)

[PDF](<https://arxiv.org/pdf/2511.20509.pdf>)