Shuffle Private Stochastic Convex Optimization
Authors: Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present interactive shuffle protocols for stochastic convex optimization. Our protocols rely on a new noninteractive protocol for summing vectors of bounded ℓ2 norm. By combining this sum subroutine with mini-batch stochastic gradient descent, accelerated gradient descent, and Nesterov s smoothing method, we obtain loss guarantees for a variety of convex loss functions that significantly improve on those of the local model and sometimes match those of the central model. Table 1: The guarantees proved in this paper. |
| Researcher Affiliation | Collaboration | Albert Cheu Georgetown University ac2305@georgetown.edu Matthew Joseph Google Research mtjoseph@google.com Jieming Mao Google Research maojm@google.com Binghui Peng Columbia University bp2601@columbia.edu |
| Pseudocode | Yes | Algorithm 1 P1D, a shuffle protocol for summing scalars, Algorithm 2 PVEC, a shuffle protocol for vector summation, Algorithm 3 PSGD, Sequentially interactive shuffle private SGD, Algorithm 4 PAGD, Sequentially interactive shuffle private AC-SA, Algorithm 5 PGD, Fully interactive shuffle private gradient descent, Algorithm 6 Pan-private AC-SA |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the methodology described, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and theoretical guarantees; it does not describe empirical studies or the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical studies, therefore, it does not specify training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs, without mentioning specific software dependencies or version numbers needed for implementation. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical guarantees; therefore, it does not provide specific experimental setup details such as hyperparameters or training configurations. |