Private Model Personalization Revisited
Authors: Conor Snedeker, Xinyu Zhou, Raef Bassily
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results in Figure 1 are obtained via data features from N(0, Id) with problem parameters n = 20, 000, d = 50, k = 2, and m = 10. Our data labels are generated as in Assumption 6 given label noise sampled from N(0, R2) with R = 0.01. We use local GD and non-private Fed Rep as baselines for our comparison. See Appendix B.4 for details1. Figure 1: Graph of population MSE over choice of privacy parameter ϵ [1, 8] for synthetic data comparing Algorithm 1 to Priv-Alt Min in (Jain et al., 2021). |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, The Ohio State University 2Department of Computer Science & Engineering and the Translational Data Analytics Institute (TDAI), The Ohio State University. Correspondence to: Conor Snedeker <EMAIL>, Xinyu Zhou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Private Fed Rep for linear regression Algorithm 2 Private Initialization for Private Fed Rep Algorithm 3 Private Representation Learning for Personalized Classification |
| Open Source Code | Yes | Note as well this Git Hub repository with a copy of our code. |
| Open Datasets | No | The results in Figure 1 are obtained via data features from N(0, Id) with problem parameters n = 20, 000, d = 50, k = 2, and m = 10. Our data labels are generated as in Assumption 6 given label noise sampled from N(0, R2) with R = 0.01. |
| Dataset Splits | Yes | Let S0 i {(xi,j, yi,j) : j [m/2]} i [n] Let S1 i Si \ S0 i i [n]. Assume for simplicity that m is even. We partition Si = S0 i S1 i where S0 i = {z1,j, . . . , z m 2 ,j} and S1 i = {z m 2 +1,j, . . . , zm,j} for each i [n]. |
| Hardware Specification | No | No specific hardware details are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | Our problem is instantiated with d = 50, k = 2, m = 10, and n = 20, 000. For Fed Rep we prune our hyperparameters, deciding on T = 5 and learning rate η = 2.5 with clipping parameter ψ = 10. Similarly, Priv-Alt Min with iterations optimized for T = 5 and clipping parameter 10 4. The Gaussian mechanism variance for both algorithms is calculated using the privacy parameter ϵ,δ = 16 log(1.25/δ) ϵ with δ = 10 6. |