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

FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment.

Application Features

• Support both deep learning and traditional machine learning algorithms (eg. PyTorch, TensorFlow, Scikit-learn, XGBoost etc.)
• Support horizontal and vertical federated learning
• Built-in Federated Learning algorithms (e.g., FedAvg, FedProx, FedOpt, Scaffold, Ditto, etc.)
• Support multiple server and client-controlled training workflows (e.g., scatter & gather, cyclic) and validation workflows (global model evaluation, cross-site validation)
• Support both data analytics (federated statistics) and machine learning lifecycle management
• Privacy preservation with differential privacy, homomorphic encryption, private set intersection (PSI)

From Simulation to Real-World

• FLARE Client API to transition seamlessly from ML/DL to FL with minimal code changes
• Simulator and POC mode for rapid development and prototyping
• Fully customizable and extensible components with modular design
• Deployment on cloud and on-premise
• Dashboard for project management and deployment
• Security enforcement through federated authorization and privacy policy
• Built-in support for system resiliency and fault tolerance

Take a look at NVIDIA FLARE Overview for a complete overview, and What's New for the lastest changes.

To install the current release:

$ python3 -m pip install nvflare

• For more details, see our getting started tutorials.
• For more advanced examples and step-by-step walk-troughs, see our examples.
• There are also detailed instructions on how to convert your standalone/centralized training code to federated learning code.
• If you'd like to write your own NVIDIA FLARE components, a detailed programming guide can be found here.

We welcome community contributions! Please refer to the contributing guidelines for more details.

Ask and answer questions, share ideas, and engage with other community members at NVFlare Discussions.

Take a look at our growing list of talks, blogs, and publications related to NVIDIA FLARE.

NVIDIA FLARE is released under an Apache 2.0 license.