This is implementation of Debiasing Model Updates for Improving Personalized Federated Training.
Please install the required packages. The code is compiled with Python 3.7 dependencies in a virtual environment via
pip install -r requirements.txt
An example code for CIFAr-10, ACID, 5 class per device setting is given. Run
python cifar10_ACID.py
The code,
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Constructs a federated dataset,
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Trains all methods,
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Plots the average test accuracy vs. rounds convergence curves.
@InProceedings{pmlr-v139-acar21a,
title = {Debiasing Model Updates for Improving Personalized Federated Training},
author = {Acar, Durmus Alp Emre and Zhao, Yue and Zhu, Ruizhao and Matas, Ramon and Mattina, Matthew and Whatmough, Paul and Saligrama, Venkatesh},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {21--31},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/acar21a/acar21a.pdf},
url = {http://proceedings.mlr.press/v139/acar21a.html}
}