flwrlabs/flower
Flower: A Friendly Federated AI Framework
What it solves
Flower 简化了构建联邦 AI 系统的过程，允许开发者在多个去中心化的设备或服务器上训练机器学习模型，而无需将原始数据移动到中心位置。
How it works
Flower 提供一个作为联邦学习编排层的框架。它被设计为框架无关（framework‑agnostic），意味着可以与任何机器学习库集成——例如 PyTorch、TensorFlow、JAX、Hugging Face 或 scikit‑learn，并且可以扩展以支持自定义策略和通信模式来分布模型训练。
Who it’s for
它面向需要构建可扩展、可定制联邦学习系统的 AI 研究人员和工程师，也适用于希望在边缘设备（如 Android、iOS 或 Raspberry Pi）上实现隐私保护 AI 的开发者。
Highlights
- Framework Agnostic: Works with almost any ML library, including PyTorch, TensorFlow, Hugging Face, and even NumPy.
- Highly Customizable: Allows users to override components to create new state-of-the-art federated systems.
- Broad Device Support: Includes quickstarts for mobile platforms (Android/TFLite, iOS/CoreML) and embedded devices (Raspberry Pi, Nvidia Jetson).
- Research-Ready: Includes "Flower Baselines," a collection of community-contributed reproductions of popular federated learning publications.