Skip to main content

Federated Learning for the Edge

Project description

Open Federated Learning (OpenFL) - An Open-Source Framework For Federated Learning

PyPI - Python Version Ubuntu CI status Windows CI status Documentation Status Downloads DockerHub PyPI version License Citation Open In Colab

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 Intel, aims to be community-driven, and welcomes contributions back to the project.


You can simply install OpenFL from PyPI:

$ pip install openfl

For more installation options check out the online documentation.

Getting Started

OpenFL enables data scientists to set up a federated learning experiment following one of the workflows:

The quickest way to test OpenFL is to follow our tutorials.
Read the blog post explaining steps to train a model with OpenFL.
Check out the online documentation to launch your first federation.


  • Ubuntu Linux 16.04 or 18.04.
  • Python 3.7+ (recommended to use with Virtualenv).

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 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.

Federated Learning


OpenFL builds on a collaboration between Intel and the University of Pennsylvania (UPenn) to develop the Federated Tumor Segmentation (FeTS, platform (grant award number: U01-CA242871).

The grant for FeTS was awarded to the Center for Biomedical Image Computing and Analytics (CBICA) at UPenn (PI: S. Bakas) from the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH).

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 UPenn on the FeTS project. An example is the FeTS-AI/Front-End, which integrates UPenn’s medical AI expertise with Intel’s 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:

Supported Aggregation Algorithms

Algorithm Name Paper PyTorch implementation TensorFlow implementation Other frameworks compatibility How to use
FedAvg McMahan et al., 2017 docs
FedProx Li et al., 2020 docs
FedOpt Reddi et al., 2020 docs
FedCurv Shoham et al., 2019 docs


Please join us for our bi-monthly community meetings starting December 1 & 2, 2022!
Meet with some of the Intel team members behind OpenFL.
We will be going over our roadmap, open for Q&A, and welcome idea sharing.

2-time slots available:

Europe Occurs every 2 months on the first Thursday of that month, 6 pm
Meeting ID: 968 5899 0725
Passcode: 157568

Asia Pacific Occurs every 2 months on the first Friday of that month, 10 AM GMT+8
Meeting ID: 989 3026 3828
Passcode: 774320

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.


	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},
	publisher={IOP Publishing}

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

openfl-1.5.tar.gz (4.3 MB view hashes)

Uploaded source

Built Distribution

openfl-1.5-py3-none-any.whl (4.5 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page