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, MXNet, scikit-learn, 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

Coming soon - curious minds can take a peek at baselines.

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.dev20211012.tar.gz (119.7 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.18.0.dev20211012-py3-none-any.whl (231.6 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.18.0.dev20211012.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.18.0.dev20211012.tar.gz
  • Upload date:
  • Size: 119.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.10 CPython/3.7.9 Linux/5.8.0-1042-azure

File hashes

Hashes for flwr-nightly-0.18.0.dev20211012.tar.gz
Algorithm Hash digest
SHA256 11e958eb66e3f83459720f86bc9a3a84a16b94c96cd0fe2ae6842c6beac685cc
MD5 94b7106ded903be2f0491dc2e1a4d577
BLAKE2b-256 5d513bbc0bc4c0a070e55ffab5b01a7f01108477e945eeffcf76bec4ac964c5c

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.18.0.dev20211012-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.18.0.dev20211012-py3-none-any.whl
Algorithm Hash digest
SHA256 0b2fa90682e7504f3bdd3dd7ca3646e620f3e3f4ebdb4cd382076a3295ea141f
MD5 e34cfab9e53954eb089bd43d1c312ace
BLAKE2b-256 d98641d2e25cb9d559ea99f8b86b08db60fc74bcf12d60e91d5ef9b251fabaf2

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