OpenFL: An open-source framework for Federated Learning
It provides a practical tool for organizations to implement federated learning in production environments, but is incremental as it builds on existing FL paradigms.
The paper introduces OpenFL, an open-source framework for federated learning that enables collaborative machine learning without sharing sensitive data, and demonstrates its application in training consensus models with international healthcare organizations and facilitating a computational competition.
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.