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NVIDIA FLARE™ (NVIDIA Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, and extensible SDK for Federated Learning. It allows researchers and data scientists to adapt existing ML/DL workflow to a federated paradigm and enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration.

GitHub Documentation

nv flare stack

Privacy-Preserving for Multi-Party Collaboration

Develop and validate more accurate and generalizable AI models from diverse data sources while mitigating the risk of compromising data security and privacy with included privacy-preserving algorithms and workflow strategies.

Accelerate AI Research

Allows researchers and data scientists to adapt existing ML/DL workflow (PyTorch, RAPIDS, Nemo, TensorFlow) to a federated learning paradigm.

Open-Source Framework

General purpose, domain-agnostic federated learning SDK that aims to create an ecosystem of developers, researchers, and data scientists.

What is Federated Learning?

Distributed Multi-Party Collaboration

Federated learning is a way to develop and validate more accurate and generalizable AI models from diverse data sources by mitigating the risk of compromising data security or privacy. It enables AI models to be built with a consortium of data providers without the data ever leaving the individual site.


Privacy-Preserving Algorithms

NVIDIA FLARE provides privacy-preserving algorithms that ensure each change to the global model stays hidden and prevent the server from reverse-engineering the submitted weights and discovering any training data.

Training and Evaluation Workflows

Built-in workflow paradigms use local and decentralized data to keep models relevant at the edge, including learning algorithms for FedAvg, FedOpt, and FedProx.

Extensible Management Tools

Management tools help secure provisioning using SSL certifications, orchestration through an admin console, and monitoring of federated learning experiments using TensorBoard for visualization.

Supports Popular ML/DL Frameworks

Flexible in design, the SDK can be used with PyTorch, Tensorflow, and even Numpy, which allows for integrating federated learning into your current workflow.

Extensive API

Its extensive and open-source API enables researchers to develop new federated workflow strategies, innovative learning, and privacy-preserving algorithms.

Reusable Building Blocks

NVIDIA FLARE provides an easy way to perform federated learning experiments by utilizing the reusable building blocks and example walkthroughs.

Who’s Using the NVIDIA Federated Learning Platform?

American College of Radiology logo
Flywheel logo
 MGH logo
Microsoft Azure logo
Rhino Health logo
Quantify logo

NVIDIA FLARE is an open-source framework available to download through the NVIDIA NVFlare GitHub Repo and PyPi.
Quick-start examples are also available on the NVIDIA FLARE documentation page.

Get Started