NVIDIA FLARE: Federated Learning from Simulation to Real-World

• DOI: 10.48550/arxiv.2210.13291
• Publication Date: 2022-01-01
• Authors: Roth, Holger R.; Cheng, Yan; Wen, Yuhong; Yang, Isaac; Xu, Ziyue; Hsieh, Yuan-Ting; Kersten, Kristopher; Harouni, Ahmed; Zhao, Can; Lu, Kevin; Zhang, Zhihong; Li, Wenqi; Myronenko, Andriy; Yang, Dong; Yang, Sean; Rieke, Nicola; Quraini, Abood; Chen, Chester; Xu, Daguang; Ma, Nic; Dogra, Prerna; Flores, Mona; Feng, Andrew
• Keywords: Networking and Internet Architecture (cs.NI); FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 02 engineering and technology; Machine Learning (cs.LG); Computer Science - Networking and Internet Architecture; Software Engineering (cs.SE); Computer Science - Software Engineering; Artificial Intelligence (cs.AI); 0202 electrical engineering, electronic engineering, information engineering

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.<br/>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<br/>