Flower_Framework.md

Flower - A Friendly Federated Learning Framework

Flower (flwr) is a 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

Flower Documentation:

• Installation
• Quickstart (TensorFlow)
• Quickstart (PyTorch)

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:

Usage Examples Documentation

Quickstart examples:

• Quickstart (TensorFlow)
• Quickstart (PyTorch)

Other examples:

• Raspberry Pi & Nvidia Jetson Tutorial
• PyTorch: From Centralized to Federated
• Advanced Flower with TensorFlow/Keras
• Single-Machine Simulation of Federated Learning Systems

Flower Baselines

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

Flower Datasets

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

Citation

If you publish work that uses Flower, please cite Flower as follows:

@article{beutel2020flower,
  title={Flower: A Friendly Federated Learning Research Framework},
  author={Beutel, Daniel J and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Parcollet, Titouan and Lane, Nicholas D},
  journal={arXiv preprint arXiv:2007.14390},
  year={2020}
}