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.
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/
Download zipfile from https://git-disl.github.io/GTDLBench/datasets/mnist_datasets/
Move all unzipped contents to ML/data/mnist.
Clean the filenames (replace dot with dash) and prep the data:
cd ML/data/mnist
mv train-labels.idx1-ubyte train-labels-idx1-ubyte
mv train-images.idx3-ubyte train-images-idx3-ubyte
mv t10k-labels.idx1-ubyte t10k-labels-idx1-ubyte
mv t10k-images.idx3-ubyte t10k-images-idx3-ubyte
python 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