{"id":17981080,"url":"https://github.com/nvidia/nvflare","last_synced_at":"2026-03-14T00:52:50.814Z","repository":{"id":36980997,"uuid":"388876833","full_name":"NVIDIA/NVFlare","owner":"NVIDIA","description":"NVIDIA Federated Learning Application Runtime 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src=\"https://raw.githubusercontent.com/NVIDIA/NVFlare/main/docs/resources/nvidia_eye.wwPt122j.png\" alt=\"NVIDIA Logo\" width=\"200\"\u003e\n\n# NVIDIA FLARE\n\n[Website](https://nvidia.github.io/NVFlare) | [Paper](https://arxiv.org/abs/2210.13291) | [Blogs](https://developer.nvidia.com/blog/tag/federated-learning) | [Talks \u0026 Papers](https://nvflare.readthedocs.io/en/main/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/main)\n\n[![Blossom-CI](https://github.com/NVIDIA/nvflare/workflows/Blossom-CI/badge.svg?branch=main)](https://github.com/NVIDIA/nvflare/actions)\n[![documentation](https://readthedocs.org/projects/nvflare/badge/?version=main)](https://nvflare.readthedocs.io/en/main/?badge=main)\n[![license](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](./LICENSE)\n[![pypi](https://badge.fury.io/py/nvflare.svg)](https://badge.fury.io/py/nvflare)\n[![pyversion](https://img.shields.io/pypi/pyversions/nvflare.svg)](https://badge.fury.io/py/nvflare)\n[![downloads](https://static.pepy.tech/badge/nvflare)](https://pepy.tech/project/nvflare)\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/NVIDIA/NVFlare)\n\n[NVIDIA FLARE](https://nvidia.github.io/NVFlare/) (**NV**IDIA **F**ederated **L**earning **A**pplication **R**untime **E**nvironment)\nis a domain-agnostic, open-source, extensible Python SDK that allows researchers and data scientists to adapt existing ML/DL workflows to a federated paradigm.\nIt enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration.\n\n## Features\nFLARE is built on a componentized architecture that allows you to take federated learning workloads\nfrom research and simulation to real-world production deployment.\n\nApplication Features\n* Support both deep learning and traditional machine learning algorithms (eg. PyTorch, TensorFlow, Scikit-learn, XGBoost etc.)\n* Support horizontal and vertical federated learning\n* Built-in Federated Learning algorithms (e.g., FedAvg, FedProx, FedOpt, Scaffold, Ditto, etc.)\n* Support multiple server and client-controlled training workflows (e.g., scatter \u0026 gather, cyclic) and validation workflows (global model evaluation, cross-site validation)\n* Support both data analytics (federated statistics) and machine learning lifecycle management\n* Privacy preservation with differential privacy, homomorphic encryption, private set intersection (PSI)\n\nFrom Simulation to Real-World\n* FLARE Client API to transition seamlessly from ML/DL to FL with minimal code changes\n* Simulator and POC mode for rapid development and prototyping\n* Fully customizable and extensible components with modular design\n* Deployment on cloud and on-premise\n* Dashboard for project management and deployment\n* Security enforcement through federated authorization and privacy policy\n* Built-in support for system resiliency and fault tolerance\n\n\u003e _Take a look at [NVIDIA FLARE Overview](https://nvflare.readthedocs.io/en/main/flare_overview.html) for a complete overview, and [What's New](https://nvflare.readthedocs.io/en/main/whats_new.html) for the lastest changes._\n\n## Installation\nTo install the [current release](https://pypi.org/project/nvflare/):\n```\n$ python -m pip install nvflare\n```\n\nFor detailed installation please refer to [NVIDIA FLARE installation](https://nvflare.readthedocs.io/en/main/installation.html).\n\n## Getting Started\n\n* To get started, refer to [getting started](https://nvflare.readthedocs.io/en/main/getting_started.html) documentation\n\n* Structured, self-paced learning is available through curated tutorials and training paths on the website.\n  * DLI courses:\n    * https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-28+V1\n    * https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-29+V1\n* visit developer portal https://developer.nvidia.com/flare\n\n## Community\n\nWe welcome community contributions! Please refer to the [contributing guidelines](./CONTRIBUTING.md) for more details.\n\nAsk and answer questions, share ideas, and engage with other community members at [NVFlare Discussions](https://github.com/NVIDIA/NVFlare/discussions).\n\n## Related Talks and Publications\n\nTake a look at our growing list of [talks and publications](https://nvflare.readthedocs.io/en/main/publications_and_talks.html), and [technical blogs](https://developer.nvidia.com/blog/tag/federated-learning) related to NVIDIA FLARE.\n\n\n## License\n\nNVIDIA FLARE is released under an [Apache 2.0 license](./LICENSE).\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnvidia%2Fnvflare","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnvidia%2Fnvflare","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnvidia%2Fnvflare/lists"}