Flower (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.

• Documentation
  • Quickstart
  • Installation

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:

Usage Examples Documentation

Available examples:

• Quickstart: Keras & MNIST
• PyTorch & CIFAR-10
• PyTorch & ImageNet
• TensorFlow & Fashion-MNIST

Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline.

Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline/dataset.

We welcome contributions. Please see CONTRIBUTING.md to get started!