English(EN) Differentially Private Model Merging

新方法无需重新训练即可实现差分隐私模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-21 04:00

研究人员开发了新的后处理方法，可以在无需重新训练的情况下创建差分隐私机器学习模型。这些技术，即随机选择和线性组合，允许在给定具有不同隐私-效用权衡的一组预先存在的模型的情况下，生成满足任何指定差分隐私要求的模型。该研究使用 R'enyi DP 和隐私损失分布提供了详细的隐私核算，并在各种数据集和模型上通过实证证明了这些方法的有效性。 AI

影响无需昂贵的重新训练即可灵活适应部署模型以应对不断变化的隐私法规。

排序理由该集群包含一篇详细介绍机器学习新研究方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Qichuan Yin, Manzil Zaheer, Tian Li · 2026-05-21 04:00

差分隐私模型合并

arXiv:2604.20985v2 Announce Type: replace-cross Abstract: In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct a magnitude of models to satisfy any targ…

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报道来源 [1]

差分隐私模型合并

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