This repository provides codes for NeurIPS 2022 paper CalFAT: Calibrated Federated Adversarial Training with Label Skewness.
The code can be run as follows.
python3 fat.py --epochs=150 --local_ep=1 --lr=0.01 --dataset=cifar10 --beta=0.1 --num_users=5
Parameter | Description |
---|---|
dataset | Dataset to use |
epochs | The total communication rounds |
local_ep | The local training epochs |
beta | The concentration parameter of the Dirichlet distribution for heterogeneous partition |
num_users | Number of clients |
lr | Learning rate |
@inproceedings{chen2022calfat,
title={CalFAT: Calibrated Federated Adversarial Training with Label Skewness},
author={Chen, Chen and Liu, Yuchen and Ma, Xingjun and Lyu, Lingjuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}