Skip to main content

Flower - A Friendly Federated Learning Framework

Project description

Flower - A Friendly Federated Learning Framework

GitHub license PRs Welcome Build Downloads Slack

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!

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-0.18.0.dev20220201.tar.gz (61.4 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.18.0.dev20220201-py3-none-any.whl (103.4 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.18.0.dev20220201.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.18.0.dev20220201.tar.gz
  • Upload date:
  • Size: 61.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.12 CPython/3.7.12 Linux/5.11.0-1027-azure

File hashes

Hashes for flwr-nightly-0.18.0.dev20220201.tar.gz
Algorithm Hash digest
SHA256 25784eff90634c3b061d77a22c74f6a278c2219aa787523e6d7a2bdbbba51d1a
MD5 37c6475a69d08e46182a56165459839f
BLAKE2b-256 d954e12d70948c6357d9768f3675c98e760362415865bca76f2ac861c5965eb8

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.18.0.dev20220201-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.18.0.dev20220201-py3-none-any.whl
Algorithm Hash digest
SHA256 13b1bf3d3cf3fac7668538a9e89a360521bccf5db0714c80a08e2a4daf89f29a
MD5 4373108b08eff806fe11394a216dc8a8
BLAKE2b-256 4ca90241a365934909c31bd113b92d336e085fb115a724606d1bb18c0c29f755

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