Flower - A Friendly Federated Learning Research Framework
flwr) - A Friendly Federated Learning Research Framework
flwr) is a research framework for building federated learning 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 Univerity of Oxford, so it was build 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, 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.
Note: Even though Flower is used in production, it is published as pre-release software. Incompatible API changes are possible.
Flower Usage Examples
A number of examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow). To run an example, first install the necessary extras:
Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline.
Contributing to Flower
We welcome contributions. Please see CONTRIBUTING.md to get started!
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