| Name | nvflare JSON |
| Version |
2.6.1
JSON |
| download |
| home_page | https://github.com/NVIDIA/NVFlare |
| Summary | Federated Learning Application Runtime Environment |
| upload_time | 2025-07-11 22:18:10 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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<img src="docs/resources/nvidia_eye.wwPt122j.png" alt="NVIDIA Logo" width="200">
# NVIDIA FLARE
[Website](https://nvidia.github.io/NVFlare) | [Paper](https://arxiv.org/abs/2210.13291) | [Blogs](https://developer.nvidia.com/blog/tag/federated-learning) | [Talks & Papers](https://nvflare.readthedocs.io/en/2.6/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/2.6)
[](https://github.com/NVIDIA/nvflare/actions)
[](https://nvflare.readthedocs.io/en/2.6/?badge=2.6)
[](./LICENSE)
[](https://badge.fury.io/py/nvflare)
[](https://badge.fury.io/py/nvflare)
[](https://pepy.tech/project/nvflare)
[NVIDIA FLARE](https://nvidia.github.io/NVFlare/) (**NV**IDIA **F**ederated **L**earning **A**pplication **R**untime **E**nvironment)
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.
## Features
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](https://nvflare.readthedocs.io/en/2.6/flare_overview.html) for a complete overview, and [What's New](https://nvflare.readthedocs.io/en/2.6/whats_new.html) for the lastest changes._
## Installation
To install the [current release](https://pypi.org/project/nvflare/):
```
$ python3 -m pip install nvflare
```
For detailed installation please refer to [NVIDIA FLARE installation](https://nvflare.readthedocs.io/en/2.6/installation.html).
## Getting Started
* To get started, visit our NVFLARE [website](https://nvidia.github.io/NVFlare/), which includes:
* Comprehensive documentation, technical blogs, tutorials, and videos
* Slides and recordings of real-world federated learning use cases from past NVFLARE Day Events.
* Tools, API guides, CLI tutorials, training materials, and extensive examples
* For hands-on learning, try our [step-by-step walkthroughs](https://github.com/NVIDIA/NVFlare/tree/2.6/examples/hello-world/step-by-step) using consistent datasets.
* Learn how to adapt your centralized training code with our guide on [converting to federated learning](https://github.com/NVIDIA/NVFlare/tree/2.6/examples/hello-world/ml-to-fl).
* Structured, self-paced learning is available through curated tutorials and training paths on the website.
* DLI courses:
* https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-28+V1
* https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-29+V1
* follow the notebooks: https://github.com/NVIDIA/NVFlare/tree/2.6/examples/tutorials/self-paced-training
* If you'd like to write your own NVIDIA FLARE components, a detailed programming guide can be found [here](https://nvflare.readthedocs.io/en/2.6/programming_guide.html).
* visit developer portal https://developer.nvidia.com/flare
## Community
We welcome community contributions! Please refer to the [contributing guidelines](./CONTRIBUTING.md) for more details.
Ask and answer questions, share ideas, and engage with other community members at [NVFlare Discussions](https://github.com/NVIDIA/NVFlare/discussions).
## Related Talks and Publications
Take a look at our growing list of [talks and publications](https://nvflare.readthedocs.io/en/2.6/publications_and_talks.html), and [technical blogs](https://developer.nvidia.com/blog/tag/federated-learning) related to NVIDIA FLARE.
## License
NVIDIA FLARE is released under an [Apache 2.0 license](./LICENSE).
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"description": "<img src=\"docs/resources/nvidia_eye.wwPt122j.png\" alt=\"NVIDIA Logo\" width=\"200\">\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 & Papers](https://nvflare.readthedocs.io/en/2.6/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/2.6)\n\n[](https://github.com/NVIDIA/nvflare/actions)\n[](https://nvflare.readthedocs.io/en/2.6/?badge=2.6)\n[](./LICENSE)\n[](https://badge.fury.io/py/nvflare)\n[](https://badge.fury.io/py/nvflare)\n[](https://pepy.tech/project/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 & 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> _Take a look at [NVIDIA FLARE Overview](https://nvflare.readthedocs.io/en/2.6/flare_overview.html) for a complete overview, and [What's New](https://nvflare.readthedocs.io/en/2.6/whats_new.html) for the lastest changes._\n\n## Installation\nTo install the [current release](https://pypi.org/project/nvflare/):\n```\n$ python3 -m pip install nvflare\n```\n\nFor detailed installation please refer to [NVIDIA FLARE installation](https://nvflare.readthedocs.io/en/2.6/installation.html).\n\n## Getting Started\n\n* To get started, visit our NVFLARE [website](https://nvidia.github.io/NVFlare/), which includes:\n * Comprehensive documentation, technical blogs, tutorials, and videos\n * Slides and recordings of real-world federated learning use cases from past NVFLARE Day Events. \n * Tools, API guides, CLI tutorials, training materials, and extensive examples\n* For hands-on learning, try our [step-by-step walkthroughs](https://github.com/NVIDIA/NVFlare/tree/2.6/examples/hello-world/step-by-step) using consistent datasets.\n* Learn how to adapt your centralized training code with our guide on [converting to federated learning](https://github.com/NVIDIA/NVFlare/tree/2.6/examples/hello-world/ml-to-fl).\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 * follow the notebooks: https://github.com/NVIDIA/NVFlare/tree/2.6/examples/tutorials/self-paced-training\n \n* If you'd like to write your own NVIDIA FLARE components, a detailed programming guide can be found [here](https://nvflare.readthedocs.io/en/2.6/programming_guide.html).\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/2.6/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",
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