nvflare 2.3.8

Federated Learning Application Runtime Environment

NVIDIA Federated Learning Application Runtime Environment

NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It 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 allows you to take federated learning workloads from research and simulation to real-world production deployment. Key components include:

• Support both deep learning and traditional machine algorithms
• Support horizontal and vertical federated learning
• Built-in FL algorithms (e.g., FedAvg, FedProx, FedOpt, Scaffold, Ditto )
• Support multiple 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
• Security enforcement through federated authorization and privacy policy
• Easily customizable and extensible
• Deployment on cloud and on premise
• Simulator for rapid development and prototyping
• Dashboard UI for simplified project management and deployment
• Built-in support for system resiliency and fault tolerance

Installation

To install the current release, you can simply run:

$ python3 -m pip install nvflare

Getting started

You can quickly get started using the FL simulator.

A detailed getting started guide is available in the documentation.

Examples and notebook tutorials are located here.

Related talks and publications

For a list of talks, blogs, and publications related to NVIDIA FLARE, see here.

License

NVIDIA FLARE has Apache 2.0 license, as found in LICENSE file.