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..
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Authors: Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across diverse datasets show that D2P2-SGD remarkably enhances accuracy while maintaining privacy. Our code is available here. ... Extensive evaluations on a wide spectrum of datasets confirm that D2P2-SGD significantly improves model accuracy compared to baseline methods. |
| Researcher Affiliation | Academia | Zhanhong Jiang# EMAIL Md Zahid Hasan** EMAIL Nastaran Saadati* EMAIL Aditya Balu# EMAIL Chao Liu*** EMAIL Soumik Sarkar*# EMAIL *Department of Mechanical Engineering, #Translational AI Center, **Department of Electrical and Computer Engineering, Iowa State University ***Department of Energy and Power Engineering, Tsinghua University |
| Pseudocode | Yes | Algorithm 1 D2P2-SGD 1: Initialize: Model parameters x1, step size α, number of epochs K, lower dimension p, random matrices A1, A2, . . . , AK, mini-batch size B, training dataset D, noise sequence σ2 ϵ,1, σ2 ϵ,2, . . . , σ2 ϵ,K, gradient clipping parameter γ 2: for k = 1, . . . , K do 3: Split the dataset D into mini-batches of size B and randomly sample one mini-batch B 4: Compute per-sample clipped gradients: ˆgs k = f(xk;s) f(xk;s) +γ , s B 5: Calculate the mini-batch stochastic gradient: gk = 1 B P s B ˆgs k 6: Project noisy gradient using Ak: gk = Ak 1 p A k gk + ϵk , ϵk N(0, σ2 ϵ,k Ip) 7: Update model parameters: xk+1 = xk α gk 8: end for 9: return x K |
| Open Source Code | Yes | Extensive experiments across diverse datasets show that D2P2-SGD remarkably enhances accuracy while maintaining privacy. Our code is available here. |
| Open Datasets | Yes | Additionally, the datasets for testing our algorithms include Fashion MNIST and SVHN Figueroa (2019). ...In Figures 8 and 9, results for the CIFAR-10 dataset are provided... ...Similarly, for Figures 11-13 (KMNIST, EMNIST, MNIST), D2P2-SGD is favorably comparable to or outperforms all baselines... |
| Dataset Splits | No | Split the dataset D into mini-batches of size B and randomly sample one mini-batch B |
| Hardware Specification | Yes | All the experiments were conducted on a machine equipped with an Intel Xeon Silver 4110 CPU and an NVIDIA Titan RTX GPU. |
| Software Dependencies | No | We leverage the Opacus library Yousefpour et al. (2021) and build the framework on top of it. |
| Experiment Setup | Yes | Table 6: Hyperparameters for experiments. Hyperparameter Value Learning rate α 0.01 Clipping parameter γ 0.01 Batch size B (256, 512, 1024) Number of Epoch K 40 Injected noise variance σϵ 3.0 Sampling variance 1 Percentage of dimensionality reduction 0.7 Number of random seeds 4 |