CalFAT: Calibrated Federated Adversarial Training on Non-IID Data

This repository provides codes for NeurIPS 2022 paper CalFAT: Calibrated Federated Adversarial Training with Label Skewness.

Running the code

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

Reference

@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}
}