Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
Authors: Puning Zhao, Jiafei Wu, Zhe Liu, Chong Wang, Rongfei Fan, Qingming Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we explore algorithms achieving optimal rates of DP optimization with heavy-tailed gradients. Our first method is a simple clipping approach... We then propose an iterative updating method... Our results match the minimax lower bound, indicating that the theoretical limit of stochastic convex optimization under DP is achievable. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China 2 Ningbo University, Ningbo, China 3 Beijing Institute of Technology, Beijing, China 4 Zhejiang University, Hangzhou, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Stochastic optimization; Algorithm 2: Simple clipping method for mean estimation; Algorithm 3: Iterative updating method for mean estimation |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and algorithm design for convex optimization problems with heavy-tailed data. It does not describe experiments using any specific dataset, nor does it provide access information for any open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets. Therefore, it does not describe any training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical, focusing on algorithm design and proofs. It does not describe any experimental evaluations that would require specific hardware, hence no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not discuss the implementation of its proposed algorithms. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not present any experimental evaluations. Consequently, there are no details regarding experimental setup, hyperparameters, or system-level training settings. |