# Power Transform Revisited: Numerically Stable, and Federated

arXiv: https://arxiv.org/abs/2510.04995 · HTML: https://mlsanity.com/paper/2510.04995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning](https://mlsanity.com/paper/2210.01639/) (2022)
- [On the Importance of Gaussianizing Representations](https://mlsanity.com/paper/2505.00685/) (2025)

## Built on by (most cited first, 1 of 1)

- [FedPS: Federated Preprocessing for structured data via aggregated Statistics](https://mlsanity.com/paper/2602.10870/) (2026)

## Abstract (arXiv)

Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to the federated learning setting, addressing both numerical and distributional challenges that arise in this context. Experiments on real-world datasets demonstrate that our methods are both effective and robust, substantially improving stability compared to existing approaches.