OpenFL: An open-source framework for Federated Learning

TL;DR

OpenFL is an open-source framework that enables privacy-preserving federated learning across organizations, supporting TensorFlow and PyTorch, and demonstrated in healthcare and competitions.

Contribution

It introduces an extensible open-source federated learning framework compatible with major ML libraries, facilitating real-world deployment and collaborative model training.

Findings

Successful training of consensus ML models in healthcare consortium

Facilitated first federated learning computational competition

Demonstrated framework's extensibility and practical application

Abstract

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.