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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Noisy SIGNSGD Is More Differentially Private Than You (Might) Think
Authors: Richeng Jin, Huaiyu Dai
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 25,709 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,457 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |