Noisy SIGNSGD Is More Differentially Private Than You (Might) Think
Authors: Richeng Jin, Huaiyu Dai
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments are conducted to validate the theoretical results. In this section, we present experimental results to validate our theoretical analyses in the previous sections. |
| Researcher Affiliation | Academia | 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China. 2Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA. |
| Pseudocode | Yes | Algorithm 1 G-Noisy Sign Compressor (Chen et al., 2020b) and Algorithm 2 Differentially Private Noisy SIGNSGD |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing the code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Datasets and Models: We evaluate the performance of the algorithms on two commonly used benchmarks for differentially private distributed learning: Fashion-MNIST (Xiao et al., 2017) and CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | The Fashion-MNIST dataset consists of 60,000 training samples and 10,000 testing samples. Each sample is a 28 × 28 size gray-level image. The CIFAR-10 dataset contains 50,000 training samples and 10,000 testing samples. Each sample is a 32 × 32 color image. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. It only mentions general experimental settings without hardware details. |
| Software Dependencies | Yes | Our experiments are mainly implemented using Python 3.8 with packages Numpy 1.19.2 and Pytorch 1.10.1. |
| Experiment Setup | Yes | Hyperparameters: The per-example gradient clipping thresholds are set to C = 1 and C = 2 for Fashion-MNIST and CIFAR-10, respectively. We tune the learning rate from the set {0.001, 0.002, 0.003, 0.005, 0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10} and run the algorithms for 500 communication rounds. We run all the algorithms for 10 repeats and present the mean test accuracy. We train both neural networks from scratch with a batch size of 32 in our experiments. |