The implementation of our paper Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
Other baseline methods are as follow:
[1] FedMix: Approximation of Mixup under Mean Augmented Federated Learning| paper| code
[2]FEDERATED OPTIMIZATION IN HETEROGENEOUS NETWORKS |paper|code
[3]Fed-TGAN: Federated learning framework for synthesizing tabular data|paper|code
[4]Generative models for effective ML on private, decentralized datasets|paper|code
Run this repo:
- generate synthetic data on Clinical dataset:
python clinical_TDA_syn.py
- run script "clinical_eval.ipynb" to evaluate the performance of data augmentation
@article{duan2022fed,
title={Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data},
author={Duan, Shaoming and Liu, Chuanyi and Han, Peiyi and He, Tianyu and Xu, Yifeng and Deng, Qiyuan},
journal={arXiv preprint arXiv:2211.13116},
year={2022}
}