flwrlabs/flower

[](https://github.com/flwrlabs/flower/blob/main/LICENSE) [](https://github.com/flwrlabs/flower/blob/main/CONTRIBUTING.md) [](https://pepy.tech/project/flwr) [](https://hub.docker.com/u/flwr) [](https://flower.ai/join-slack) Flower (flwr) is a framework for building federated AI systems. The design of Flower is based on a few guiding principles: another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. 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. 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. community is encouraged to both read and contribute to the codebase. Meet the Flower community on flower.ai! Flower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated…