# FederalLearning

**Repository Path**: kun-god/FederalLearning

## Basic Information

- **Project Name**: FederalLearning
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-04
- **Last Updated**: 2023-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Personalized Federated Learning using Hypernetworks
This is an official implementation of ***Personalized Federated Learning using Hypernetworks*** paper. [[Link]](https://arxiv.org/abs/2103.04628)

![](resources/pfedhn_arch.png)

#### 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.

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#### Reproduce Paper Results

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##### PfedHN Results on CIFAR10
- Run: ```cd experiments/pfedhn```
- Run: ```python trainer.py```

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##### 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:

```bib
@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}
}
```