Skip to content

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

Notifications You must be signed in to change notification settings

zhongjian-zhang/FederalLearning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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}
}

About

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

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •  

Languages