Flower: A Friendly Federated AI Framework

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Flower (flwr) is a framework for building federated AI systems. The design of Flower is based on a few guiding principles:

• Customizable: Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case.

• Extendable: Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems.

• Framework-agnostic: Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, CatBoost, LeRobot for federated robots, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand.

• Understandable: Flower is written with maintainability in mind. The community is encouraged to both read and contribute to the codebase.

Meet the Flower community on flower.ai!

Federated Learning Tutorial

Flower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated learning and how to implement them in Flower.

0. What is Federated Learning?

1. Get started with Flower

2. Write your first Flower App

3. Write your first Flower App with PyTorch

4. Use a federated learning strategy

5. Customize a Flower Strategy

6. Communicate Custom Messages

Stay tuned, more tutorials are coming soon. Topics include Privacy and Security in Federated Learning, and Scaling Federated Learning.

Documentation

Flower Docs:

• Installation
• Quickstart (TensorFlow)
• Quickstart (PyTorch)
• Quickstart (Hugging Face)
• Quickstart (PyTorch Lightning)
• [Quickstart (Pandas)](h