# Private Model Personalization Revisited

arXiv: https://arxiv.org/abs/2506.19220 · HTML: https://mlsanity.com/paper/2506.19220/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2025.

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

- [Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication](https://mlsanity.com/paper/2609.15950/) (2026)

## Abstract (arXiv)

We study model personalization under user-level differential privacy (DP) in the shared representation framework. In this problem, there are n users whose data is statistically heterogeneous, and their optimal parameters share an unknown embedding U^* ∈ℝd× k that maps the user parameters in ℝd to low-dimensional representations in ℝk, where kll d. Our goal is to privately recover the shared embedding and the local low-dimensional representations with small excess risk in the federated setting. We propose a private, efficient federated learning algorithm to learn the shared embedding based on the FedRep algorithm in [CHM+21]. Unlike [CHM+21], our algorithm satisfies differential privacy, and our results hold for the case of noisy labels. In contrast to prior work on private model personalization [JRS+21], our utility guarantees hold under a larger class of users' distributions (sub-Gaussian instead of Gaussian distributions). Additionally, in natural parameter regimes, we improve the privacy error term in [JRS+21] by a factor of widetildeO(dk). Next, we consider the binary classification setting. We present an information-theoretic construction to privately learn the shared embedding and derive a margin-based accuracy guarantee that is independent of d. Our method utilizes the Johnson-Lindenstrauss transform to reduce the effective dimensions of the shared embedding and the users' data. This result shows that dimension-independent risk bounds are possible in this setting under a margin loss.