Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Private Model Personalization Revisited
Authors: Conor Snedeker, Xinyu Zhou, Raef Bassily
ICML 2025 | Venue PDF | 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. |