This is the reference code for implementing our Learning-to-Attack-Federated-Learning framework. Paper link
utilities.py contains all helper functions and defense algorithms including median, clipping median, krum and FLtrust.
exp_environments.py contains all environments used for training attacker policy.
denoise.py contains code for training autoencoder which is used in distribution learning.
sim_train.py contains code for distribution learning and policy learning.
test.py contains code for testing all other baselines including no attack, inner product manipulation(IPM), explicit boosting(EB), local model poisoning attack(LMP)
Please run the following command to install required packages
# requirements
pip install -r requirements.txt
#You can change dataset in denoise.py to train your own autoencoder and replace the autoencoder directory in sim_train.py
python3 denoise.py
#Default settings in sim_train.py are MNIST, Clipping Median and q = 0.1, you can change these settings in sim_train.py and select coresponding environment from exp_experiments.py
python3 sim_train.py
#Since we do not perform distribution learning for CIFAR-10 dataset, we seperate policy training for CIFAR-10 here.
python3 cifar_train.py
#Change the model dir to your own experiment, you can change dataset and defense method to match your own experiment
python3 test.py
@inproceedings{ li2022learning, title={Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework}, author={Henger Li and Xiaolin Sun and Zizhan Zheng}, booktitle={Advances in Neural Information Processing Systems}, editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho}, year={2022}, url={https://openreview.net/forum?id=4OHRr7gmhd4} }
Our inverting gradients implementation is modified from https://github.com/JonasGeiping/invertinggradients
The implementation of FL system is based on https://github.com/adap/flower (Flower - A Friendly Federated Learning Framework)