Differentially Private Online-to-batch for Smooth Losses
Authors: Qinzi Zhang, Hoang Tran, Ashok Cutkosky
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our algorithm on three different datasets: two benchmark datasets (MNIST and CIFAR-10) and one real-world dataset (Adult). In this section, we present numerical results on the three datasets. |
| Researcher Affiliation | Collaboration | Sayan Das, Arpit Agarwal, Andrew R. Cohen MIT Lincoln Laboratory, Anand D. Sarwate Rutgers University, ECE Dept. |
| Pseudocode | Yes | Algorithm 1 DP-Online-to-Batch, Algorithm 2 Private Online Gradient Descent (DP-OGD) |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of our algorithm on three different datasets: two benchmark datasets (MNIST and CIFAR-10) and one real-world dataset (Adult). |
| Dataset Splits | Yes | For MNIST, we split the 60,000 samples into 50,000 training samples and 10,000 testing samples. For CIFAR-10, we use 50,000 training samples and 10,000 testing samples. For the Adult dataset, we split the 48842 samples into 32561 training samples and 16281 testing samples. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | Our code is implemented in PyTorch. The paper does not specify the version of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For MNIST and CIFAR-10, we train our model for 500 epochs with a batch size of 128. For the Adult dataset, we train for 1000 epochs with a batch size of 128. The learning rate for DP-SGD and DP-OGD is set to 0.01. The step size η for Algorithm 1 is set to 100. |