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, 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.
翻译:联邦学习(FL)通过利用多个合作者提供的各种数据集,而无需集中数据,能够建立强大和普遍适用的AI模型。我们创建了NVIDIA FLARE,作为开放源码软件开发工具包(SDK),使数据科学家更容易在其研究和现实世界应用中使用FL;SDK包括最新FL算法和联合机学习方法的解决方案,这些解决方案有助于建立工作流程,在企业之间传播学习,并使平台开发者能够利用同质加密或差异性隐私权,为多党合作创造安全、隐私保护提议。SDK是一个轻巧、灵活和可扩缩的Python软件包,使研究人员能够将其数据科学工作流程引入任何培训图书馆(PyTorrch、TensorFlow、XGBoost,甚至NumPy),并将其应用于现实世界FLL环境。本文介绍了FLARE的关键设计原则,并展示了一些使用案例(例如COVID分析)。SD是一个定制的FL工作流程,可以应用不同的隐私/NVIVA.C守则。