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Byzantine Attack-Resistant Federated Averaging Based on Outlier Elimination

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ARFED: Attack Resistant Federated Averaging Based on Outlier Elimination

This repository is the official implementation of ARFED: Attack Resistant Federated Averaging Based on Outlier Elimination. The framework and full methodology are detailed in our manuscript.

Install required packages

pip install -r requirements.txt

for pip environment. OR

conda env create -f environment.yml

To create a conda virtual environment from an environment.yml file. Please see here for more details.

How to run

We performed the experiments for label flipping and Byzantine attacks on MNIST, Fashion MNIST, and CIFAR10 datasets. We compared our results with the median method. The file name list for each data set is as follows. The default setting of experiments is NON-IID organized with a 20% malicious participant ratio.

Dataset Attack Type Method File Name
MNIST Byzantine ARFED mnist_byz_barfed.py
MNIST Byzantine Median mnist_byz_median.py
MNIST Byzantine Trimmed Mean mnist_byz_trimmed.py
MNIST Byzantine No Defense mnist_byz_attack.py
MNIST Label Flipping ARFED mnist_lf_barfed.py
MNIST Label Flipping Median mnist_lf_median.py
MNIST Label Flipping Trimmed Mean mnist_lf_trimmed.py
MNIST Label Flipping No Defense mnist_byz_attack.py
MNIST Partial Knowledge ARFED mnist_partial_knowledge_barfed.py
MNIST Partial Knowledge Median mnist_partial_knowledge_median.py
MNIST Partial Knowledge Trimmed Mean mnist_partial_knowledge_trimmed.py
MNIST Partial Knowledge No Defense mnist_partial_knowledge_attack.py
Fashion MNIST Byzantine ARFED fashion_byz_barfed.py
Fashion MNIST Byzantine Median fashion_byz_median.py
Fashion MNIST Byzantine Trimmed Mean fashion_byz_trimmed.py
Fashion MNIST Byzantine No Defense fashion_byz_attack.py
Fashion MNIST Label Flipping ARFED fashion_lf_barfed.py
Fashion MNIST Label Flipping Median fashion_lf_median.py
Fashion MNIST Label Flipping Trimmed Mean fashion_lf_trimmed.py
Fashion MNIST Label Flipping No Defense fashion_lf_attack.py
Fashion MNIST Partial Knowledge ARFED fashion_partial_knowledge_barfed.py
Fashion MNIST Partial Knowledge Median fashion_partial_knowledge_median.py
Fashion MNIST Partial Knowledge Trimmed Mean fashion_partial_knowledge_trimmed.py
Fashion MNIST Partial Knowledge No Defense fashion_partial_knowledge_attack.py
CIFAR10 Byzantine ARFED cifar_byz_barfed.py
CIFAR10 Byzantine Median cifar_byz_median.py
CIFAR10 Byzantine Trimmed Mean cifar_byz_trimmed.py
CIFAR10 Byzantine No Defense cifar_byz_attack.py
CIFAR10 Label Flipping ARFED cifar_lf_barfed.py
CIFAR10 Label Flipping Median cifar_lf_median.py
CIFAR10 Label Flipping Trimmed Mean cifar_lf_trimmed.py
CIFAR10 Label Flipping No Defense cifar_lf_attack.py
CIFAR10 Partial Knowledge ARFED cifar_partial_knowledge_barfed.py
CIFAR10 Partial Knowledge Median cifar_partial_knowledge_median.py
CIFAR10 Partial Knowledge Trimmed Mean cifar_partial_knowledge_trimmed.py
CIFAR10 Partial Knowledge No Defense cifar_partial_knowledge_attack.py

Citation

Please use the following BibTeX entry for citation:

@article{ISIKPOLAT2023626,
title = {ARFED: Attack-Resistant Federated averaging based on outlier elimination},
journal = {Future Generation Computer Systems},
volume = {141},
pages = {626-650},
year = {2023},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2022.12.003},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X22004083},
author = {Ece Isik-Polat and Gorkem Polat and Altan Kocyigit}
}

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