In this repository, code is for our ICLR 2020 paper DBA: Distributed Backdoor Attacks against Federated Learning
Install Pytorch
- download the raw dataset lending-club-loan-data.zip into dir
./utils - preprocess the dataset.
cd ./utils
./process_loan_data.sh
- download the dataset tiny-imagenet-200.zip into dir
./utils - reformat the dataset.
cd ./utils
./process_tiny_data.sh
MNIST and CIFAR will be automatically download
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prepare the pretrained model: Our pretrained clean models for attack can be downloaded from Google Drive. You can also train from the round 0 to obtain the pretrained clean model.
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we can use Visdom to monitor the training progress.
python -m visdom.server -p 8098
- run experiments for the four datasets:
python main.py --params utils/X.yaml
X = mnist_params, cifar_params,tiny_params or loan_params. Parameters can be changed in those yaml files to reproduce our experiments.
Stay tuned for further updates, thanks!
If you find our work useful in your research, please consider citing:
@inproceedings{
xie2020dba,
title={DBA: Distributed Backdoor Attacks against Federated Learning},
author={Chulin Xie and Keli Huang and Pin-Yu Chen and Bo Li},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rkgyS0VFvr}
}