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.
Begin your NVIDIA FLARE journey with these guides designed to help you quickly grasp essential concepts. Follow along with the videos below, and try it out yourself.
Try out these example code blocks below, where we showcase how simple it is to adapt a popular machine learning framework to a federated learning scenario with NVIDIA FLARE. For more details, refer to the getting started walkthrough guide above.
These progressive tutorial series cover various aspects of NVIDIA FLARE from core concepts and basic tools to advanced algorithms and deployments. For a comprehensive list of all tutorials, view the tutorial catalog below.
Provides user-friendly APIs for client and server programming. Use the Simulator and POC modes to quickly simulate a federated learning application.
Designed as a federated computing platform agnostic to frameworks, workloads, datasets, and domains. Federated learning apps are built on this foundation.
Flexible open architecture allows for extensive customization, with a modular design that ensures each layer can be easily pluggable with custom components.
Prioritizes security with secure provisioning, event-based security plugins, authorization control, data filtering, audit logs, and advanced privacy-preserving algorithms.
Enables a seamless integration with external 3rd-party systems with the FlareAgent to receive tasks and submit results to the NVIDIA FLARE server.
Supports cross-cloud deployment with various management tools. Designed for robust, production-scale deployment in real-world federated learning scenarios.
Federated Learning is a distributed learning paradigm where training occurs across multiple clients, each with their own local datasets. This enables the creation of common robust models without sharing sensitive local data, helping solve issues of data privacy and security.
NVIDIA Federated Learning Technical Blogs covering various applications and topics.
List of NVIDIA FLARE related papers and publications along with videos from blogs and talks.
