nvflare-light 2.6.0rc2
Federated Learning Application Runtime Environment
Requires Python: >=3.8

<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/main/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/main)

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[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/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._

## Installation
To install the [current release](https://pypi.org/project/nvflare/):
```
$ python3 -m pip install nvflare
```
## Getting Started

### To get started, take a look at the instructions and code examples on our [**website**](https://nvidia.github.io/NVFlare/).

* For more details, see our getting started [tutorials](./examples/getting_started/README.md).
* For more advanced examples and [step-by-step](./examples/hello-world/step-by-step/README.md) walk-troughs, see our [examples](examples).
* There are also detailed instructions on how to convert your standalone/centralized training code to [federated learning code](./examples/hello-world/ml-to-fl/README.md).
* If you'd like to write your own NVIDIA FLARE components, a detailed programming guide can be found [here](https://nvflare.readthedocs.io/en/main/programming_guide.html).

## Community

We welcome community contributions! Please refer to the [contributing guidelines](https://github.com/NVIDIA/NVFlare/blob/main/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, blogs, and publications](https://nvflare.readthedocs.io/en/main/publications_and_talks.html) related to NVIDIA FLARE.

## License

NVIDIA FLARE is released under an [Apache 2.0 license](https://github.com/NVIDIA/NVFlare/blob/main/LICENSE).