English(EN) Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

全同态加密下的机器学习训练获得收敛性保证和隐私保护

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-27 00:09

研究人员开发了一种使用全同态加密（FHE）训练机器学习模型的新方法，该方法允许在不解密的情况下对加密数据进行计算。该方法首次为全同态加密下的机器学习训练提供了理论收敛性保证，并整合了差分隐私。与标准的差分隐私梯度下降相比，新算法在计算效率上更高，通过使用多项式近似激活函数和损失函数，并避免了昂贵的每样本梯度裁剪，从而实现了可比的效用。 AI

影响通过提高加密训练模型的效率，使得在敏感数据上进行更安全、更私密的机器学习成为可能。

排序理由该集群包含一篇详细介绍机器学习训练新算法和理论分析的学术论文。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Yvonne Zhou, Mingyu Liang, Ivan Brugere, Danial Dervovic, Yue Guo, Antigoni Polychroniadou, Min Wu, Dana Dachman-Soled · 2026-05-28 04:00

重新审视全同态加密下的机器学习训练：收敛性保证、差分隐私和高效算法

arXiv:2605.27782v1 Announce Type: new Abstract: We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training algorithm tailored to encrypted computation. Our appro…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-27 00:09

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training algorithm tailored to encrypted computation. Our approach improves computational efficiency over stand…

报道来源 [2]

重新审视全同态加密下的机器学习训练：收敛性保证、差分隐私和高效算法

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

相关实体

相关话题