OpenFL: An open-source framework for Federated Learning

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.

References in corpus (9)

• TensorFlow: A system for large-scale machine learning
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• The Future of Digital Health with Federated Learning
• Confounding variables can degrade generalization performance of radiological deep learning models
• Federated Machine Learning: Concept and Applications
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection
• The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
• Vertical Federated Learning: Challenges, Methodologies and Experiments
• Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

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