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!
You can simply install OpenFL from PyPI:
$ pip install openfl
For more installation options check out the online documentation.
OpenFL supports two APIs to set up a Federated Learning experiment:
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Task Runner API: Define an experiment and distribute it manually. All participants can verify model code and FL plan prior to execution. The federation is terminated when the experiment is finished. This API is meant for enterprise-grade FL experiments, including support for mTLS-based communication channels and TEE-ready nodes (based on Intel® SGX).
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Workflow API (experimental): Create complex experiments that extend beyond traditional horizontal federated learning. This API enables an experiment to be simulated locally, then seamlessly scaled to a federated setting. See the experimental tutorials to learn how to coordinate aggregator validation after collaborator model training, perform global differentially private federated learning, measure the amount of private information embedded in a model after collaborator training with privacy meter, or add a watermark to a federated model.
The quickest way to test OpenFL is to follow the online documentation to launch your first federation.
Read the blog post explaining steps to train a model with OpenFL.
OpenFL supports popular NumPy-based ML frameworks like TensorFlow, PyTorch and Jax which should be installed separately.
Users can extend the list of supported Machine Learning frameworks if needed.
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.
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) platform (grant award number: U01-CA242871).
The grant for FeTS was awarded from the Informatics Technology for Cancer Research (ITCR) 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) at UPenn and now heading up the Division of Computational Pathology at Indiana University (IU).
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, 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
- Reina A, et al., 2021
- Sheller MJ, et al., 2020
- Sheller MJ, et al., 2019
- Yang Y, et al., 2019
- McMahan HB, et al., 2016
| Algorithm Name | Paper | PyTorch implementation | TensorFlow implementation | Other frameworks compatibility |
|---|---|---|---|---|
| FedAvg | McMahan et al., 2017 | ✅ | ✅ | ✅ |
| FedProx | Li et al., 2020 | ✅ | ✅ | ❌ |
| FedOpt | Reddi et al., 2020 | ✅ | ✅ | ✅ |
| FedCurv | Shoham et al., 2019 | ✅ | ❌ | ❌ |
The OpenFL community is growing, and we invite you to be a part of it. Join the Slack channel to connect with fellow enthusiasts, share insights, and contribute to the future of federated learning.
Consider subscribing to the OpenFL mail list [email protected]
See you there!
We also always welcome questions, issue reports, and suggestions via:
This project is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
@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}
}