NVIDIA FLARE¶

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

NVIDIA FLARE is built on a componentized architecture that gives you the flexibility to take federated learning workloads from research and simulation to real-world production deployment. Some of the key components of this architecture include:

FL Simulator for rapid development and prototyping

FLARE Dashboard for simplified project management and deployment

Reference FL algorithms (e.g., FedAvg, FedProx) and workflows (e.g., Scatter and Gather, Cyclic)

Privacy preservation with differential privacy, homomorphic encryption, and more

Management tools for secure provisioning and deployment, orchestration, and management

Specification-based API for extensibility

Learn more about FLARE features in the FLARE Overview and What’s New.

Getting Started¶

To get started with NVIDIA FLARE:

Follow the Installation guide to set up your environment
Run through the Quickstart guide to try your first example
Explore more examples in the Example Applications section

For first-time users and FL researchers, FLARE provides the FL Simulator that allows you to build, test, and deploy applications locally.

FLARE for Users¶

If you want to learn how to interact with the FLARE system, please refer to the User Guide. When you are ready for a secure, distributed deployment, the Real World Federated Learning section covers the tools and processes required to deploy and operate a secure, real-world FLARE project.

FLARE for Developers¶

When you’re ready to build your own application, the Programming Guide, Programming Best Practices, FAQ, and API Reference provide an in-depth look at the FLARE platform and APIs.