CONTRA (ESORICS 2021)

Code for "CONTRA: Defending against Poisoning Attacks in Federated Learning" published in ESORICS 2021.

• Set configuration parameters in utils/mnist_params.yaml for MNIST or utils/cifar_params.yaml for CIFAR-10 experiments

• For example, run the code for MNIST using the following:

python main.py --params utils/mnist_params.yaml

• To run RFL.py, do:

python RFL.py -d mnist -N 10 -A 2 -cuda

Citation:

• If you find the work useful for your research, please cite:

@inproceedings{awan2021contra,
  title={Contra: Defending against poisoning attacks in federated learning},
  author={Awan, Sana and Luo, Bo and Li, Fengjun},
  booktitle={Computer Security--ESORICS 2021: 26th European Symposium on Research in Computer Security, Darmstadt, Germany, October 4--8, 2021, Proceedings, Part I 26},
  pages={455--475},
  year={2021},
  organization={Springer}
}

References:

• https://github.com/DistributedML/FoolsGold
• https://github.com/AI-secure/DBA
• https://github.com/XinyiYS/Robust-and-Fair-Federated-Learning