NVIDIA FLARE: Federated Learning from Simulation to Real-World
Abstract
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.
Cite
@article{arxiv.2210.13291,
title = {NVIDIA FLARE: Federated Learning from Simulation to Real-World},
author = {Holger R. Roth and Yan Cheng and Yuhong Wen and Isaac Yang and Ziyue Xu and Yuan-Ting Hsieh and Kristopher Kersten and Ahmed Harouni and Can Zhao and Kevin Lu and Zhihong Zhang and Wenqi Li and Andriy Myronenko and Dong Yang and Sean Yang and Nicola Rieke and Abood Quraini and Chester Chen and Daguang Xu and Nic Ma and Prerna Dogra and Mona Flores and Andrew Feng},
journal= {arXiv preprint arXiv:2210.13291},
year = {2023}
}
Comments
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 submission