Flower 框架文档¶
Welcome to Flower's documentation. Flower is a friendly federated learning framework.
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Flower 社区发展迅速--我们是一个由研究人员、工程师、学生、专业人士、学者和其他爱好者组成的友好团体。
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Flower 框架¶
The user guide is targeted at researchers and developers who want to use Flower to bring existing machine learning workloads into a federated setting. One of Flower's design goals was to make this simple. Read on to learn more.
教程¶
以学习为导向的联邦学习教程系列，最好的起点。
QUICKSTART TUTORIALS: PyTorch | TensorFlow | MLX | 🤗 Transformers | JAX | Pandas | fastai | PyTorch Lightning | scikit-learn | XGBoost | Android | iOS
How-to Guides¶
以问题为导向的 "如何做 "指南逐步展示如何实现特定目标。
How-to Guides
- Build
- Simulate
- Deploy
- Run Flower with the Deployment Engine
- Enable TLS connections
- Authenticate SuperNodes
- Configure logging
- Run Flower on Azure
- Run Flower using Docker
- Quickstart with Docker
- Enable TLS for Secure Connections
- Persist the State of the SuperLink
- Set Environment Variables
- Run with Root User Privileges
- Run ServerApp or ClientApp as a Subprocess
- Pin a Docker Image to a Specific Version
- Use a Different Flower Version
- Quickstart with Docker Compose
- Run Flower Quickstart Examples with Docker Compose
- Deploy Flower on Multiple Machines with Docker Compose
说明¶
以理解为导向的概念指南解释并讨论了Flower和协作式人工智能背后的关键主题和基本思想。
参考资料¶
Contribute¶
Flower 社区欢迎您的贡献。以下文档旨在为您提供帮助。