FederalLearning
通过阅读Communication-Efficient Learning of Deep Networks from Decentralized Data与Robust and Communication-Efficient Federated Learning from Non-IID Data两篇论文,复现FedAvg与STC算法,完成LSTM模型+ Shakespeare数据集的字符预测任务
Install / Use
/learn @zhongjian-zhang/FederalLearningREADME
Personalized Federated Learning using Hypernetworks
This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [Link]

Installation
- Create a virtual environment with conda/virtualenv
- Clone the repo
- Run:
cd <PATH_TO_THE_CLONED_REPO> - Run:
pip install -e .to install necessary packages and path links.
Reproduce Paper Results
PfedHN Results on CIFAR10
- Run:
cd experiments/pfedhn - Run:
python trainer.py
PfedHN-PC Results on CIFAR10
- Run:
cd experiments/pfedhn_pc - Run:
python trainer.py
Citation
If you find pFedHN to be useful in your own research, please consider citing the following paper:
@article{shamsian2021personalized,
title={Personalized Federated Learning using Hypernetworks},
author={Shamsian, Aviv and Navon, Aviv and Fetaya, Ethan and Chechik, Gal},
journal={arXiv preprint arXiv:2103.04628},
year={2021}
}
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