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.

翻译：联邦学习（FL）通过利用来自多个合作者的多样化数据集，在不集中数据的情况下，构建稳健且泛化能力强的AI模型。我们创建了NVIDIA FLARE这一开源软件开发工具包（SDK），旨在使数据科学家更易于在研究和实际应用中使用联邦学习。该SDK包含最先进的联邦学习算法和联邦机器学习方法的解决方案，有助于构建跨企业的分布式学习工作流，并使平台开发者能够利用同态加密或差分隐私，为多方协作创建安全且保护隐私的解决方案。该SDK是一个轻量级、灵活且可扩展的Python包。它允许研究人员在实际的联邦学习环境中，使用任何训练库（如PyTorch、TensorFlow、XGBoost甚至NumPy）应用其数据科学工作流。本文介绍了NVFlare的关键设计原则，并通过实现不同隐私保护算法的可定制联邦学习工作流，展示了若干用例（例如COVID分析）。代码详见https://github.com/NVIDIA/NVFlare。