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OpenFL: the open federated learning library
Patrick Foley1, Micah J Sheller1, Brandon Edwards1
1Intel Corporation, Santa Clara, CA 95052, United States of America.
Physics in Medicine and Biology
|October 5, 2022
Summary
Open Federated Learning (OpenFL) enables collaborative machine learning without data sharing. This open-source framework facilitates secure, scalable model training across diverse applications, including healthcare.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Federated learning (FL) allows collaborative model training without sharing sensitive data.
- Organizations need secure and scalable solutions for privacy-preserving machine learning.
Purpose of the Study:
- Introduce the Open Federated Learning (OpenFL) framework.
- Demonstrate OpenFL's applicability in real-world scenarios and its potential beyond healthcare.
Main Methods:
- OpenFL is an open-source Python framework supporting TensorFlow and PyTorch.
- It facilitates the migration of centralized ML models to a federated training pipeline.
- Recommendations for securing federations using trusted execution environments are provided.
Main Results:
- OpenFL enables data-private collaborative learning for machine learning (ML) and deep learning (DL).
- The framework supports scalable, trusted execution for production environments.
- Successful real-world healthcare federations using OpenFL are described.
Conclusions:
- OpenFL is a versatile tool for federated learning applicable across various domains.
- It prioritizes scalability, security, and ease of migration for ML models.
- The library is readily available for research and production adoption.
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