解决幂变换数值不稳定的难题，支持联邦学习场景。
Power Transform Revisited: Numerically Stable, and Federated
- 分析幂变换数值不稳定的根源并提出修复方案。
- 在真实数据集上验证方法显著提升稳定性。
- 首次将幂变换扩展至联邦学习，兼顾数值与分布挑战。
幂变换是使数据更接近高斯分布的常用参数化方法，广泛用于统计分析和机器学习的预处理。然而我们发现，直接实现的幂变换存在严重的数值不稳定性，可能导致错误结果甚至程序崩溃。本文系统分析了这些不稳定的来源，并提出有效的解决方案。进一步将幂变换拓展到联邦学习场景，解决了该环境下出现的数值与分布双重挑战。在真实数据集上的实验表明，所提方法既有效又鲁棒，相比现有方法显著提升了稳定性。
原文摘要 · Abstract (English)
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.
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