Flower - A Friendly Federated Learning Framework

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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, Hugging Face Transformers, PyTorch Lightning, MXNet, scikit-learn, TFLite, 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.

Meet the Flower community on flower.dev!

Documentation

Flower Docs:

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:

Other examples:

Flower Baselines / Datasets

Experimental - curious minds can take a peek at baselines.

Community

Flower is built by a wonderful community of researchers and engineers. Join Slack to meet them, contributions are welcome.

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!

Metadata

Release files for flwr-nightly 0.18.0.dev20220225

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for flwr-nightly 0.18.0.dev20220225
File	Size	Uploaded
flwr-nightly-0.18.0.dev20220225.tar.gz	64.3 kB	Feb 25, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for flwr-nightly 0.18.0.dev20220225
File	Interpreter	ABI	Platform	Reset
flwr_nightly-0.18.0.dev20220225-py3-none-any.whl 106.3 kB Feb 25, 2022	Python 3	none	any	Details

Total release size: 170.7 kB

Release files / flwr-nightly-0.18.0.dev20220225.tar.gz

Download URL	flwr-nightly-0.18.0.dev20220225.tar.gz
Size	64.3 kB
Tags	Source
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BLAKE2b-256 checksum How to use checksums	`19fd0872a023fe35b8ba6c04278bd5571630140aef411df56690118b5be4590b`
Upload date	Feb 25, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.1.12 CPython/3.7.12 Linux/5.11.0-1028-azure`

Release files / flwr_nightly-0.18.0.dev20220225-py3-none-any.whl

Download URL	flwr_nightly-0.18.0.dev20220225-py3-none-any.whl
Size	106.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`ef3ec70b2561d94e6fa167e873a1f7a307521d407721d0f62bf0a7cd42388e2a`
BLAKE2b-256 checksum How to use checksums	`00de189022c1085d0af4cfe11cda9f184e13f9506831b6be9fa3757d97c3dc43`
Upload date	Feb 25, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.1.12 CPython/3.7.12 Linux/5.11.0-1028-azure`