CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference
Authors: Wenxuan Zeng, Meng Li, Haichuan Yang, Wen-jie Lu, Runsheng Wang, Ru Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Co Priv with the SOTA 2PC protocol, Cryp TFlow2, and demonstrate 2.1 communication reduction for both Res Net-18 and Res Net32 on CIFAR-100. We also compare Co Priv with SOTA network optimization methods, including SNL, Meta Pruning, etc. Co Priv achieves 9.98 and 3.88 online and total communication reduction with a higher accuracy compared to SNL, respectively. Co Priv also achieves 3.87 online communication reduction with more than 3% higher accuracy compared to Meta Pruning. |
| Researcher Affiliation | Collaboration | Wenxuan Zeng Peking University zwx.andy@stu.pku.edu.cn Meng Li Peking University meng.li@pku.edu.cn Haichuan Yang Meta AI haichuan@meta.com Wen-jie Lu Ant Group juhou.lwj@antgroup.com Runsheng Wang Peking University r.wang@pku.edu.cn Ru Huang Peking University ruhuang@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1: Network Re-parameterization for Inverted Residual Block |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a direct link to a code repository for its methodology. |
| Open Datasets | Yes | We apply Co Priv to Mobile Net V2 with different width multipliers on CIFAR-100 [30] and Image Net [9] datasets. |
| Dataset Splits | No | The paper mentions using CIFAR-100 and ImageNet datasets, but it does not provide specific training, validation, or test split percentages, sample counts, or explicit references to predefined standard splits. |
| Hardware Specification | Yes | All of our experiments are evaluated on the Intel Xeon Gold 5220R CPU @ 2.20GHz. |
| Software Dependencies | No | The paper mentions software like 'Cyp TFlow2', 'Eigen', and 'Armadillo matrix calculation library' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We first search and prune redundant Re LUs for 10 epochs and then finetune the pruned network for 180 epochs with stochastic gradient descent (SGD) optimizer [2], cosine learning scheduler and initial learning rate of 0.1. |