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.
@article{arxiv.2105.06413,
title = {OpenFL: An open-source framework for Federated Learning},
author = {G Anthony Reina and Alexey Gruzdev and Patrick Foley and Olga Perepelkina and Mansi Sharma and Igor Davidyuk and Ilya Trushkin and Maksim Radionov and Aleksandr Mokrov and Dmitry Agapov and Jason Martin and Brandon Edwards and Micah J. Sheller and Sarthak Pati and Prakash Narayana Moorthy and Shih-han Wang and Prashant Shah and Spyridon Bakas},
journal= {arXiv preprint arXiv:2105.06413},
year = {2022}
}