Skip to main content

Flower - A Friendly Federated Learning Framework

Project description

Flower - A Friendly Federated Learning Framework

GitHub license PRs Welcome Build Downloads

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:

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

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-0.14.0.dev20210208.tar.gz (107.9 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.14.0.dev20210208-py3-none-any.whl (212.4 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.14.0.dev20210208.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.14.0.dev20210208.tar.gz
  • Upload date:
  • Size: 107.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.4 CPython/3.7.9 Linux/5.4.0-1039-azure

File hashes

Hashes for flwr-nightly-0.14.0.dev20210208.tar.gz
Algorithm Hash digest
SHA256 e6de79dc3e80cf3a64d898bca50c926249ed5bc5487c51f18d2e0c0be329c75b
MD5 0114be22515d9e6155318756cd68c4ed
BLAKE2b-256 8d5c790ea7173dcc5bd8174e280b718cfaf3e4b6e5566a4c1e84c636d360e58c

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.14.0.dev20210208-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.14.0.dev20210208-py3-none-any.whl
Algorithm Hash digest
SHA256 cc824a3564b191fe1c71b2dfadfb4e5b83c96d797f5d061739f35431d596352b
MD5 5409c86c249386cd20625302c7236f67
BLAKE2b-256 df595cefb5c88f008190d70c10d919e7dd5c003ab023cceb959eedec6d8a1655

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