NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adaptexisting 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 in the FLARE Overview, Key Features, What’s New, and the User Guide and Programming Guide.

Getting Started

For first-time users and FL researchers, FLARE provides the NVIDIA FLARE FL Simulator that allows you to build, test, and deploy applications locally. The Getting Started guide covers installation and walks through an example application using the FL Simulator.

When you are ready to for a secure, distributed deployment, the Real World Federated Learning section covers the tools and process 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 Best Practices, FAQ, and Programming Guide give an in depth look at the FLARE platform and APIs.