/backdoor_federated_learning

Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

Primary LanguagePythonMIT LicenseMIT

11/20/2020: We are developing a new framework for backdoors with FL: Backdoors101. It extends to many new attacks (clean-label, physical backdoors, etc) and has improved user experience. Check it out!

backdoor_federated_learning

This code includes experiments for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

All experiments are done using Python 3.7 and PyTorch 1.0.

mkdir saved_models

python training.py --params utils/params.yaml

I encourage to contact me (eugene@cs.cornell.edu) or raise Issues in GitHub, so I can provide more details and fix bugs.

Most of the experiments resulted by tweaking parameters in utils/params.yaml (for images) and utils/words.yaml (for text), you can play with them yourself.

Reddit dataset