NVIDIA FLARE: Federated Learning from Simulation to Real-World

TL;DR

NVIDIA FLARE is an open-source SDK that simplifies the implementation of federated learning across diverse real-world applications, supporting multiple algorithms and privacy-preserving techniques.

Contribution

The paper introduces NVIDIA FLARE, a flexible, scalable SDK that enables researchers and developers to easily deploy federated learning with privacy features in practical settings.

Findings

Supports multiple machine learning frameworks like PyTorch and TensorFlow.

Includes privacy-preserving algorithms such as homomorphic encryption and differential privacy.

Demonstrated use cases like COVID data analysis.

Abstract

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.