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arxiv: 2210.13291 · v3 · pith:5BFWT7C2new · submitted 2022-10-24 · 💻 cs.LG · cs.AI· cs.CV· cs.NI· cs.SE

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Holger R. Roth , Yan Cheng , Yuhong Wen , Isaac Yang , Ziyue Xu , Yuan-Ting Hsieh , Kristopher Kersten , Ahmed Harouni
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Can Zhao Kevin Lu Zhihong Zhang Wenqi Li Andriy Myronenko Dong Yang Sean Yang Nicola Rieke Abood Quraini Chester Chen Daguang Xu Nic Ma Prerna Dogra Mona Flores Andrew Feng
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classification 💻 cs.LG cs.AIcs.CVcs.NIcs.SE
keywords learningdatafederatednvidiareal-worldworkflowsalgorithmsbuilding
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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.

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