A sybil-resilient distributed learning protocol that penalizes Sybils based on their gradient similarity.
- Peer-reviewed conference paper pdf:
"The Limitations of Federated Learning in Sybil Settings."
Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
To appear in 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID) 2020.
Bibtex:
@InProceedings{Fung2020,
title = {{The Limitations of Federated Learning in Sybil Settings}},
author = {Clement Fung and Chris J. M. Yoon and Ivan Beschastnikh},
year = {2020},
series = {RAID},
booktitle = {Symposium on Research in Attacks, Intrusion, and Defenses},
}
Download and gunzip files from http://yann.lecun.com/exdb/mnist/
Move all the outputted files to ML/data/mnist.
Navigate to that directory: cd ML/data/mnist and run parse_mnist.py
From main directory navigate to the ML directory: cd ML/
Run: python code/misslabel_dataset.py mnist 1 7
From main directory navigate to the ML directory: cd ML/
And run the following command for a 5 sybil, 1-7 attack on mnist.
python code/ML_main.py mnist 1000 5_1_7