Differentially Private Model Personalization
Authors: Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give new algorithms for this setting, analyze their accuracy on specific data distributions, and test our results empirically. |
| Researcher Affiliation | Collaboration | Prateek Jain Google Research prajain@google.com Keith Rush Google Research krush@google.com Adam Smith Boston University ads22@bu.edu Shuang Song Google Research shuangsong@google.com Abhradeep Thakurta Google Research athakurta@google.com |
| Pseudocode | Yes | Algorithm 1 APriv-Alt Min: Differentially Private Alternating Minimization Meta-algorithm |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions using 'synthetic data' but does not provide any link, DOI, or formal citation for public access to this data. |
| Dataset Splits | No | The paper describes generating synthetic data and evaluating population MSE, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for overall model evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We set the number of users n = 50, 000, number of samples per user m = 10, data dimension d = 50 and rank k = 2. We sample x, U and v from Gaussian distributions, and the field noise σF of target y is set to be 0.01. We normalize U to unit norm. We run Algorithm 1 with full batch, i.e., T = 1 and for multiple epochs. [...] We fix the clipping norm to be 10 4 and pick the optimal the number of epochs in {1, 2, 5, 10}. |