flwrlabs/flower
Flower: A Friendly Federated AI Framework
What it solves
Flower 簡化了構建聯邦 AI 系統的流程，讓開發者能在多個去中心化的裝置或伺服器上訓練機器學習模型，而無需將原始資料搬移到中心位置。
How it works
Flower 提供一個作為聯邦學習編排層的框架。它被設計為框架無關（framework‑agnostic），意味著可以與任何機器學習函式庫整合——例如 PyTorch、TensorFlow、JAX、Hugging Face 或 scikit‑learn，且可擴充以支援自訂策略與通訊模式來分散模型訓練。
Who it’s for
它適用於需要構建可擴展、可自訂聯邦學習系統的 AI 研究人員與工程師，也適合想在邊緣裝置（如 Android、iOS 或 Raspberry Pi）上實作隱私保護 AI 的開發者。
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.