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https://github.com/apple/pfl-research
Simulation framework for accelerating research in Private Federated Learning
https://github.com/apple/pfl-research
differential-privacy federated-learning federated-learning-framework machine-learning privacy
Last synced: 6 days ago
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Simulation framework for accelerating research in Private Federated Learning
- Host: GitHub
- URL: https://github.com/apple/pfl-research
- Owner: apple
- License: apache-2.0
- Created: 2023-12-15T22:39:21.000Z (about 1 year ago)
- Default Branch: develop
- Last Pushed: 2024-12-15T05:37:46.000Z (about 1 month ago)
- Last Synced: 2025-01-05T00:07:47.724Z (13 days ago)
- Topics: differential-privacy, federated-learning, federated-learning-framework, machine-learning, privacy
- Language: Jupyter Notebook
- Homepage: http://apple.github.io/pfl-research/
- Size: 3.32 MB
- Stars: 308
- Watchers: 21
- Forks: 32
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
README
# `pfl`: Python framework for Private Federated Learning simulations
[](https://github.com/apple/pfl-research/blob/main/LICENSE)
[](https://dl.circleci.com/status-badge/redirect/gh/apple/pfl-research/tree/main)
[](https://github.com/apple/pfl-research/blob/main/pyproject.toml#L18)
**Documentation website:** https://apple.github.io/pfl-research
`pfl` is a Python framework developed at Apple to empower researchers to run efficient simulations with privacy-preserving federated learning (FL) and disseminate the results of their research in FL. We are a team comprising engineering and research expertise, and we encourage researchers to publish their papers, with this code, with confidence.
The framework is `not` intended to be used for third-party FL deployments but the results of the simulations can be tremendously useful in actual FL deployments.
We hope that `pfl` will promote open research in FL and its effective dissemination.
``pfl`` provides several useful features, including the following:
* Get started quickly trying out PFL for your use case with your existing model and data.
* Iterate quickly with fast simulations utilizing multiple levels of distributed training (multiple processes, GPUs and machines).
* Flexibility and expressiveness - when a researcher has a PFL idea to try, ``pfl`` has flexible APIs to express these ideas.
* Scalable simulations for large experiments with state-of-the-art algorithms and models.
* Support both PyTorch and TensorFlow.
* Unified benchmarks for datasets that have been vetted for both PyTorch and TensorFlow.
* Support other models in addition to neural networks, e.g. GBDTs. Switching between types of models is seamless.
* Tight integration with privacy features, including common mechanisms for local and central differential privacy.
Results from benchmarks are maintained in [this Weights & Biases report](https://api.wandb.ai/links/pfl/5scd5f66).
## Installation
Installation instructions can be found [here](http://apple.github.io/pfl-research/installation.html).
`pfl` is available on PyPI and a full installation be done with pip:
```
pip install 'pfl[tf,pytorch,trees]'
```
## Getting started - tutorial notebooks
To try out `pfl` immediately without installation, we provide several colab notebooks for learning the different components in `pfl` hands-on.
* [](https://colab.research.google.com/github/apple/pfl-research/blob/develop/tutorials/Introduction%20to%20Federated%20Learning%20with%20CIFAR10%20and%20TensorFlow.ipynb) Introduction to Federated Learning with CIFAR10 and TensorFlow.
* [](https://colab.research.google.com/github/apple/pfl-research/blob/develop/tutorials/Introduction%20to%20PFL%20research%20with%20FLAIR.ipynb) Introduction to PFL research with FLAIR and PyTorch.
* [](https://colab.research.google.com/github/apple/pfl-research/blob/develop/tutorials/Introduction%20to%20Differential%20Privacy%20with%20Federated%20Learning.ipynb) Introduction to Differential Privacy (DP) with Federated Learning.
* [](https://colab.research.google.com/github/apple/pfl-research/blob/develop/tutorials/Creating%20Federated%20Dataset%20for%20PFL%20Experiment.ipynb) Creating Federated Dataset for PFL Experiment.
We also support MLX!
* ([Jupyter notebook](https://github.com/apple/pfl-research/blob/develop/tutorials/Introduction%20to%20Federated%20Learning%20with%20CIFAR10%20and%20MLX.ipynb)) Introduction to Federated Learning with CIFAR10 and MLX.
But you have to run this notebook locally on your Apple silicon, see all Jupyter notebooks available [here](https://github.com/apple/pfl-research/tree/develop/tutorials).
## Getting started - benchmarks
`pfl` aims to streamline the benchmarking process of testing hypotheses in the Federated Learning paradigm. The official benchmarks are available in the [benchmarks](./benchmarks) directory, using a variety of realistic dataset-model combinations with and without differential privacy (yes, we do also have CIFAR10).
**Copying these examples is a great starting point for doing your own research.**
[See the quickstart](./benchmarks#quickstart) on how to start converging a model on the simplest benchmark (CIFAR10) in just a few minutes.
## Contributing
Researchers are invited to contribute to the framework. Please, see [here](http://apple.github.io/pfl-research/support/contributing.html) for more details.
## Citing pfl-research
```
@article{granqvist2024pfl,
title={pfl-research: simulation framework for accelerating research in Private Federated Learning},
author={Granqvist, Filip and Song, Congzheng and Cahill, {\'A}ine and van Dalen, Rogier and Pelikan, Martin and Chan, Yi Sheng and Feng, Xiaojun and Krishnaswami, Natarajan and Jina, Vojta and Chitnis, Mona},
journal={arXiv preprint arXiv:2404.06430},
year={2024},
}
```