Code for simulating C-VFL, a communication-efficient algorithm for vertically partitioned data. More details on the algorithm can be in our paper: Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data:
@inproceedings{castiglia2022compressed,
title={Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data},
author={Castiglia, Timothy and Das, Anirban and Wang, Shiqiang and Patterson, Stacy},
booktitle={International Conference on Machine Learning},
year={2022}
}
One can install our environment with Anaconda:
conda env create -f flearn.yml
'ModelNet_CVFL': contains code for running C-VFL with the ModelNet10 and CIFAR-10 datasets
'ImageNet_CVFL': contains code for running C-VFL with the ImageNet dataset
'mimic3_CVFL': contains code for running C-VFL with the MIMIC-III dataset
Information on how to run C-VFL are provided in the README's in each folder.