Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Authors: Roie Reshef, Kfir Yehuda Levy
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran DP-µ2on MNIST using a logistic regression model in the untrusted server case. The parameters are G = 2 785 = 39.6, L = 785/2 = 392.5, D = 0.1, which brings us S = 118.1. Our model has d = 10 785 = 7850 parameters. We kept M T = 60,000, and checked M = 1, 10, 100 and ρ = 4, 8, 16. We show our results in Table 1. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Technion, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 DP-µ2-FL for Untrusted Server Algorithm 2 DP-µ2-FL for Trusted Server |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code for the described methodology. |
| Open Datasets | Yes | We ran DP-µ2on MNIST using a logistic regression model in the untrusted server case. |
| Dataset Splits | No | The paper mentions 'MNIST' and that 'M T = 60,000' but does not specify the explicit percentages or sample counts for training, validation, and test splits, nor does it refer to a standard split method with a citation. |
| Hardware Specification | No | The paper mentions running experiments but does not specify any hardware details like GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper describes the algorithms and their theoretical properties but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We ran DP-µ2on MNIST using a logistic regression model in the untrusted server case. The parameters are G = 2 785 = 39.6, L = 785/2 = 392.5, D = 0.1, which brings us S = 118.1. Our model has d = 10 785 = 7850 parameters. We kept M T = 60,000, and checked M = 1, 10, 100 and ρ = 4, 8, 16. |