Related papers: Differentially Private Model Merging

Differentially Private Model Merging

URL: http://arxiv.org/abs/2604.20985v1
Date: Wed, 22 Apr 2026 18:13:37 GMT
Title: Differentially Private Model Merging
Abstract summary: We generate a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training steps.<n>We propose two post processing techniques, namely random selection and linear combination, to output a final private model for any target privacy parameter.
Score: 23.141132405899096
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In machine learning applications, privacy requirements during inference or deployment time could change constantly due to varying policies, regulations, or user experience. In this work, we aim to generate a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training steps, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post processing techniques, namely random selection and linear combination, to output a final private model for any target privacy parameter. We provide privacy accounting of these approaches from the lens of R'enyi DP and privacy loss distributions for general problems. In a case study on private mean estimation, we fully characterize the privacy/utility results and theoretically establish the superiority of linear combination over random selection. Empirically, we validate our approach and analyses on several models and both synthetic and real-world datasets.

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