Skip to main content

Flower - A Friendly Federated Learning Research Framework

Project description

Flower (flwr) - A Friendly Federated Learning Research Framework

GitHub license PRs Welcome Build

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:

Usage Examples Documentation

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!

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

flwr-nightly-0.8.0.dev20201009.tar.gz (92.3 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.8.0.dev20201009-py3-none-any.whl (178.6 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.8.0.dev20201009.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.8.0.dev20201009.tar.gz
  • Upload date:
  • Size: 92.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.0.10 CPython/3.7.9 Linux/5.4.0-1026-azure

File hashes

Hashes for flwr-nightly-0.8.0.dev20201009.tar.gz
Algorithm Hash digest
SHA256 cfb26c3e1837e99eaaa60d8bbadb8f857d41dd2ae22c7e89f0a2d75566799e47
MD5 ed48009c36caa8c355bcad531432f0cb
BLAKE2b-256 425487c102e5b41ebebc5d666784c48d22e4d6d1582cc0d770ec398476205071

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.8.0.dev20201009-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.8.0.dev20201009-py3-none-any.whl
Algorithm Hash digest
SHA256 f3b3cf084a56dadc90f12620d81e7a899fc3b88eedfc63578ab5a1137c8519a7
MD5 733bffcfd82a89f7484a2e69862f1f7f
BLAKE2b-256 98c8eaf401b5aec4678a53de170213b2e37356e25c8a0411560b2b0dda757aa7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page