NVIDIA FLARE: Federated Learning from Simulation to Real-WorldDownload PDF

Published: 21 Oct 2022, Last Modified: 21 Jul 2024FL-NeurIPS 2022 PosterReaders: Everyone
Keywords: Federated Learning, Systems and Infrastructure, Open-Source, Python
TL;DR: We present FLARE, an open-source SDK to make it easier for data scientists to use federated learning in their research and in the real-world.
Abstract: Federated learning (FL) enables the building of 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.)
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Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/nvidia-flare-federated-learning-from/code)
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