NVIDIA/NVFlare

NVIDIA のプロフィール

記録された最新のソース活動: 2026年9月3日 22:23

SkillsMP カタログ更新: 2026年10月17日 00:09

収集済み skills: 11

GitHub スター: 981
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<img src="https://raw.githubusercontent.com/NVIDIA/NVFlare/main/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) | [Webinars](https://nvidia.github.io/NVFlare/webinars) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/main) [![Blossom-CI](https://github.com/NVIDIA/nvflare/workflows/Blossom-CI/badge.svg?branch=main)](https://github.com/NVIDIA/nvflare/actions) [![documentation](https://readthedocs.org/projects/nvflare/badge/?version=main)](https://nvflare.readthedocs.io/en/main/?badge=main) [![license](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](./LICENSE) [![pypi](https://badge.fury.io/py/nvflare.svg)](https://badge.fury.io/py/nvflare) [![pyversion](https://img.shields.io/pypi/pyversions/nvflare.svg)](https://badge.fury.io/py/nvflare) [![downloads](https://static.pepy.tech/badge/nvflare)](https://pepy.tech/project/nvflare) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/NVIDIA/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 (e.g., 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 latest changes._ ## Installation To install the [current release](https://pypi.org/project/nvflare/): ``` $ python -m pip install nvflare ``` For detailed installation please refer to [NVIDIA FLARE installation](https://nvflare.readthedocs.io/en/main/installation.html). ## Getting Started * To get started, refer to the [Quick Start](https://nvflare.readthedocs.io/en/main/quickstart.html) documentation * 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 * Visit the [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/main/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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