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..
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 | Venue PDF | 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 EMAIL Meng Li Peking University EMAIL Haichuan Yang Meta AI EMAIL Wen-jie Lu Ant Group EMAIL Runsheng Wang Peking University EMAIL Ru Huang Peking University EMAIL |
| 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. |