NVIDIA FLARE: Federated Learning from Simulation to Real-World

Holger R Roth, Yan Cheng, Yuhong Wen, Isaac Yang, Ziyue Xu, YuanTing Hsieh, Kristopher Kersten, Ahmed Harouni, Can Zhao, Kevin Lu, Zhihong Zhang, Wenqi Li, Andriy Myronenko, Dong Yang, Sean Yang, Nicola Rieke, Abood Quraini, Chester Chen, Daguang Xu, Nic Ma et al. (3 additional authors not shown)

Keywords: Federated Learning, Systems and Infrastructure, Open-Source, Python

TL;DR: We present FLARE, an open-source SDK to make it easier for data scientists to use federated learning in their research and in the real-world.

Abstract:

Federated learning (FL) enables the building of 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, and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) and apply them in real-world FL settings. This paper introduces the key design principles of FLARE 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.)

Is Student: No

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.13291/code)