Learning-to-Attack-Federated-Learning

This is the reference code for implementing our Learning-to-Attack-Federated-Learning framework. Paper link

Code Structure

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)

Setup Environment

Please run the following command to install required packages

# requirements
pip install -r requirements.txt

Autoencoder

#You can change dataset in denoise.py to train your own autoencoder and replace the autoencoder directory in sim_train.py
python3 denoise.py

Distribution Learning and Policy Learning

#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

CIFAR-10

#Since we do not perform distribution learning for CIFAR-10 dataset, we seperate policy training for CIFAR-10 here.
python3 cifar_train.py

Test

#Change the model dir to your own experiment, you can change dataset and defense method to match your own experiment
python3 test.py

Cite

@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} }

References

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)