论文标题
NVIDIA FLARE：从模拟到现实世界的联合学习
NVIDIA FLARE: Federated Learning from Simulation to Real-World
论文作者
论文摘要
联合学习（FL）通过利用来自多个合作者的不同数据集而不集中数据来构建可靠和可推广的AI模型。我们创建了NVIDIA Flare作为开源软件开发套件（SDK），以使数据科学家更容易在其研究和实际应用中使用FL。 SDK包括针对最先进的FL算法和联合机器学习方法的解决方案，这些解决方案促进了跨企业的分布式学习的建筑工作流程，并使平台开发人员能够为多派对合作提供使用同源性加密或差异隐私或差异隐私。 SDK是轻巧，灵活和可扩展的Python包装。它允许研究人员在现实世界中的任何培训库（Pytorch，Tensorflow，Xgboost甚至Numpy）中应用其数据科学工作流程。本文介绍了NVFlare的关键设计原理，并用可自定义的FL工作流程说明了一些用例（例如，COVID分析），可实现不同的隐私保护算法。 代码可从https://github.com/nvidia/nvflare获得。
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.
