OpenFL: An open-source framework for Federated Learning
G. Anthony Reina, Alexey Gruzdev, Patrick Foley, Olga S. Perepelkina, Mansi Sharma, I. V. Davidyuk, Ilya Trushkin, Maksim Radionov, Aleksandr Mokrov, Dmitry Agapov, Jason Martin, Brandon Edwards, Micah Sheller, Sarthak Pati, Prakash Narayana Moorthy, Wang, Shih-han, Prashant Shah, Spyridon Bakas · arXiv (Cornell University) · 2021
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.