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https://github.com/securefederatedai/openfl
An open framework for Federated Learning.
https://github.com/securefederatedai/openfl
collaborative-learning deep-learning distributed-computing distributed-learning fedavg fedcurv federated-analytics federated-deep-learning federated-learning federated-learning-framework fedopt fedprox keras-federated-learning machine-learning openfl privacy-preserving-machine-learning python pytorch-federated-learning secure-computation tensorflow-federated-learning
Last synced: 3 months ago
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An open framework for Federated Learning.
- Host: GitHub
- URL: https://github.com/securefederatedai/openfl
- Owner: securefederatedai
- License: apache-2.0
- Created: 2021-01-12T21:29:52.000Z (about 4 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-29T08:59:23.000Z (3 months ago)
- Last Synced: 2024-10-29T10:07:03.911Z (3 months ago)
- Topics: collaborative-learning, deep-learning, distributed-computing, distributed-learning, fedavg, fedcurv, federated-analytics, federated-deep-learning, federated-learning, federated-learning-framework, fedopt, fedprox, keras-federated-learning, machine-learning, openfl, privacy-preserving-machine-learning, python, pytorch-federated-learning, secure-computation, tensorflow-federated-learning
- Language: Jupyter Notebook
- Homepage: https://openfl.readthedocs.io/en/latest/index.html
- Size: 134 MB
- Stars: 720
- Watchers: 22
- Forks: 200
- Open Issues: 118
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION
- Security: SECURITY.md
- Governance: GOVERNANCE.md
- Roadmap: ROADMAP.md
Awesome Lists containing this project
- awesome-PETs - OpenFL - Open-source FL framework hosted by the Linux Foundation. (Privacy-Preserving Federated Learning / Open-Source Tools)
- awesome-production-machine-learning - OpenFL - OpenFL is a Python framework for Federated Learning. OpenFL is designed to be a _flexible_, _extensible_ and _easily learnable_ tool for data scientists. OpenFL is developed by Intel Internet of Things Group (IOTG) and Intel Labs. (Privacy and Security)
README
[](https://pypi.org/project/openfl/)
[](https://github.com/securefederatedai/openfl/actions/workflows/ubuntu.yml)
[](https://github.com/securefederatedai/openfl/actions/workflows/windows.yml)
[](https://openfl.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/openfl)
[](https://hub.docker.com/r/intel/openfl)
[](https://pypi.org/project/openfl/)
[
](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
[](https://opensource.org/licenses/Apache-2.0)
[](https://arxiv.org/abs/2105.06413)
[](https://bestpractices.coreinfrastructure.org/projects/6599)
[](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/interactive_api/numpy_linear_regression/workspace/SingleNotebook.ipynb)
Open Federated Learning (OpenFL) is a Python 3 framework for Federated Learning. OpenFL is designed to be a _flexible_, _extensible_ and _easily learnable_ tool for data scientists. OpenFL is hosted by The Linux Foundation, aims to be community-driven, and welcomes contributions back to the project.
Looking for the Open Flash Library project also referred to as OpenFL? Find it [here](https://github.com/openfl/openfl)!
## Installation
You can simply install OpenFL from PyPI:
```
$ pip install openfl
```
For more installation options check out the [online documentation](https://openfl.readthedocs.io/en/latest/get_started/installation.html).
## Getting Started
OpenFL enables data scientists to set up a federated learning experiment following one of the workflows:
- [Director-based Workflow](https://openfl.readthedocs.io/en/latest/about/features_index/interactive.html):
Setup long-lived components to run many experiments in series. Recommended for FL research when many changes to model, dataloader, or hyperparameters are expected
- [Aggregator-based Workflow](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html):
Define an experiment and distribute it manually. All participants can verify model code and [FL plan](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html#federated-learning-plan-fl-plan-settings) prior to execution. The federation is terminated when the experiment is finished
- [Workflow Interface](https://openfl.readthedocs.io/en/latest/about/features_index/workflowinterface.html) ([*experimental*](https://openfl.readthedocs.io/en/latest/developer_guide/experimental_features.html)):
Create complex experiments that extend beyond traditional horizontal federated learning. See the [experimental tutorials](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb).
The quickest way to test OpenFL is to follow our [tutorials](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials).
Read the [blog post](https://towardsdatascience.com/go-federated-with-openfl-8bc145a5ead1) explaining steps to train a model with OpenFL.
Check out the [online documentation](https://openfl.readthedocs.io/en/latest/index.html) to launch your first federation.
## Requirements
- Ubuntu Linux 18.04+
- Python 3.7+ (recommended to use with [Virtualenv](https://virtualenv.pypa.io/en/latest/)).
OpenFL supports training with TensorFlow 2+ or PyTorch 1.3+ which should be installed separately. User can extend the list of supported Deep Learning frameworks if needed.
## Project Overview
### What is Federated Learning
[Federated learning](https://en.wikipedia.org/wiki/Federated_learning) is a distributed machine learning approach that enables collaboration on machine learning projects without having to share sensitive data, such as, patient records, financial data, or classified information. The minimum data movement needed across the federation is solely the model parameters and their updates.
### Background
OpenFL builds on a collaboration between Intel and the Bakas lab at the University of Pennsylvania (UPenn) to develop the [Federated Tumor Segmentation (FeTS, www.fets.ai)](https://www.fets.ai/) platform (grant award number: U01-CA242871).
The grant for FeTS was awarded from the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), to Dr Spyridon Bakas (Principal Investigator) when he was affiliated with the [Center for Biomedical Image Computing and Analytics (CBICA)](https://www.cbica.upenn.edu/) at UPenn and now heading up the [Division of Computational Pathology at Indiana University (IU)](https://medicine.iu.edu/pathology/research/computational-pathology).
FeTS is a real-world medical federated learning platform with international collaborators. The original OpenFederatedLearning project and OpenFL are designed to serve as the backend for the FeTS platform, and OpenFL developers and researchers continue to work very closely with IU on the FeTS project. An example is the [FeTS-AI/Front-End](https://github.com/FETS-AI/Front-End), which integrates the group’s medical AI expertise with OpenFL framework to create a federated learning solution for medical imaging.
Although initially developed for use in medical imaging, OpenFL designed to be agnostic to the use-case, the industry, and the machine learning framework.
You can find more details in the following articles:
- [Pati S, et al., 2022](https://www.nature.com/articles/s41467-022-33407-5)
- [Reina A, et al., 2021](https://arxiv.org/abs/2105.06413)
- [Sheller MJ, et al., 2020](https://www.nature.com/articles/s41598-020-69250-1)
- [Sheller MJ, et al., 2019](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589345)
- [Yang Y, et al., 2019](https://arxiv.org/abs/1902.04885)
- [McMahan HB, et al., 2016](https://arxiv.org/abs/1602.05629)
### Supported Aggregation Algorithms
| Algorithm Name | Paper | PyTorch implementation | TensorFlow implementation | Other frameworks compatibility | How to use |
| -------------- | ----- | :--------------------: | :-----------------------: | :----------------------------: | ---------- |
| FedAvg | [McMahan et al., 2017](https://arxiv.org/pdf/1602.05629.pdf) | ✅ | ✅ | ✅ | [docs](https://openfl.readthedocs.io/en/latest/about/features.html#aggregation-algorithms) |
| FedProx | [Li et al., 2020](https://arxiv.org/pdf/1812.06127.pdf) | ✅ | ✅ | ❌ | [docs](https://openfl.readthedocs.io/en/latest/about/features.html#aggregation-algorithms) |
| FedOpt | [Reddi et al., 2020](https://arxiv.org/abs/2003.00295) | ✅ | ✅ | ✅ | [docs](https://openfl.readthedocs.io/en/latest/about/features.html#aggregation-algorithms) |
| FedCurv | [Shoham et al., 2019](https://arxiv.org/pdf/1910.07796.pdf) | ✅ | ❌ | ❌ | [docs](https://openfl.readthedocs.io/en/latest/about/features.html#aggregation-algorithms) |
## Support
Please join us for our bi-monthly community meetings starting December 1 & 2, 2022!
Meet with some of the OpenFL team members behind OpenFL.
We will be going over our roadmap, open for Q&A, and welcome idea sharing.
Calendar and links to a Community calls are [here](https://wiki.lfaidata.foundation/pages/viewpage.action?pageId=70648254)
Subscribe to the OpenFL mail list [email protected]
See you there!
We also always welcome questions, issue reports, and suggestions via:
* [GitHub Issues](https://github.com/securefederatedai/openfl/issues)
* [Slack workspace](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
## License
This project is licensed under [Apache License Version 2.0](LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
## Citation
```
@article{openfl_citation,
author={Foley, Patrick and Sheller, Micah J and Edwards, Brandon and Pati, Sarthak and Riviera, Walter and Sharma, Mansi and Moorthy, Prakash Narayana and Wang, Shi-han and Martin, Jason and Mirhaji, Parsa and Shah, Prashant and Bakas, Spyridon},
title={OpenFL: the open federated learning library},
journal={Physics in Medicine \& Biology},
url={http://iopscience.iop.org/article/10.1088/1361-6560/ac97d9},
year={2022},
doi={10.1088/1361-6560/ac97d9},
publisher={IOP Publishing}
}
```