Repo for 'Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience'
Other baselines can be found at: https://github.com/tamlhp/awesome-machine-unlearning
Please read and cite our paper:
Huynh, T.T., Nguyen, T.B., Nguyen, P.L., Nguyen, T.T., Weidlich, M., Nguyen, Q.V.H. and Aberer, K., 2024. Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience. arXiv preprint arXiv:2405.18040.
@article{huynh2024fast,
title={Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience},
author={Huynh, Thanh Trung and Nguyen, Trong Bang and Nguyen, Phi Le and Nguyen, Thanh Tam and Weidlich, Matthias and Nguyen, Quoc Viet Hung and Aberer, Karl},
journal={arXiv preprint arXiv:2405.18040},
year={2024}
}
First, run the command below to get the ARDIS dataset (which is used as backdoor data for MNIST dataset):
# change to the ARDIS_DATASET_IV folder
cd customdata/ARDIS_DATASET_IV
# unrar ARDIS_DATASET
unrar e ARDIS_DATASET_IV.rar
# return to the root folder
cd ...Second, run the command below to get the splited dataset MNIST:
bash gen_data.shThe splited data will be stored in ./fedtask/mnist_cnum25_dist0_skew0_seed0/attack.json.
Third, run the command below to quickly run the experiment on MNIST dataset:
# all parameters are set in the file run_exp.sh
bash run_exp.shThe result will be stored in ./fedtasksave/mnist_cnum25_dist0_skew0_seed0/R51_P0.30_alpha0.07/record/history51.pkl.
Finally, run the command below to return accuracy:
# return accuracy
python test_unlearn.py
# Main accuracy: ...
# Backdoor accuracy: ...