Flower - A Friendly Federated Learning Research Framework
Project description
Flower (flwr
) - A Friendly Federated Learning Research Framework
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
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:
Available examples:
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}
}
Please also consider adding your publication to the list of Flower-based publications in the docs, just open a Pull Request.
Contributing to Flower
We welcome contributions. Please see CONTRIBUTING.md to get started!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for flwr-nightly-0.8.0.dev20201009.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | cfb26c3e1837e99eaaa60d8bbadb8f857d41dd2ae22c7e89f0a2d75566799e47 |
|
MD5 | ed48009c36caa8c355bcad531432f0cb |
|
BLAKE2b-256 | 425487c102e5b41ebebc5d666784c48d22e4d6d1582cc0d770ec398476205071 |
Hashes for flwr_nightly-0.8.0.dev20201009-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f3b3cf084a56dadc90f12620d81e7a899fc3b88eedfc63578ab5a1137c8519a7 |
|
MD5 | 733bffcfd82a89f7484a2e69862f1f7f |
|
BLAKE2b-256 | 98c8eaf401b5aec4678a53de170213b2e37356e25c8a0411560b2b0dda757aa7 |