3 research outputs found
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models
by leveraging diverse datasets from multiple collaborators without centralizing
the data. We created NVIDIA FLARE as an open-source software development kit
(SDK) to make it easier for data scientists to use FL in their research and
real-world applications. The SDK includes solutions for state-of-the-art FL
algorithms and federated machine learning approaches, which facilitate building
workflows for distributed learning across enterprises and enable platform
developers to create a secure, privacy-preserving offering for multiparty
collaboration utilizing homomorphic encryption or differential privacy. The SDK
is a lightweight, flexible, and scalable Python package. It allows researchers
to apply their data science workflows in any training libraries (PyTorch,
TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper
introduces the key design principles of NVFlare and illustrates some use cases
(e.g., COVID analysis) with customizable FL workflows that implement different
privacy-preserving algorithms.
Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS
2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022);
Revised version v2: added Key Components list, system metrics for homomorphic
encryption experiment; Extended v3 for journal submissio
Federated learning for predicting clinical outcomes in patients with COVID-19
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare