.. toctree:: :maxdepth: -1 :hidden: :caption: Introduction fl_introduction flare_overview whats_new Getting Started <quickstart>
.. toctree:: :maxdepth: -1 :hidden: :caption: Guides example_applications_algorithms real_world_fl user_guide programming_guide best_practices
.. toctree:: :maxdepth: -1 :hidden: :caption: Miscellaneous faq publications_and_talks contributing API <apidocs/modules> glossary
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 :ref:`FLARE Overview <flare_overview>` and :ref:`What's New <whats_new>`.
For first-time users and FL researchers, FLARE provides the :ref:`FL Simulator <fl_simulator>` that allows you to build, test, and deploy applications locally. The :ref:`Getting Started <getting_started>` guide covers installation and walks through an example application using the FL Simulator. Additional examples can be found at the :ref:`Examples Applications <example_applications>`, which showcase different federated learning workflows and algorithms on various machine learning and deep learning tasks.
If you want to learn how to interact with the FLARE system, please refer to the :ref:`User Guide <user_guide>`. When you are ready to for a secure, distributed deployment, the :ref:`Real World Federated Learning <real_world_fl>` section covers the tools and process required to deploy and operate a secure, real-world FLARE project.
When you're ready to build your own application, the :ref:`Programming Guide <programming_guide>`, :ref:`Programming Best Practices <best_practices>`, :ref:`FAQ<faq>`, and :ref:`API Reference <apidocs/modules>` give an in depth look at the FLARE platform and APIs.