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-0.15.0.dev20210225.tar.gz (109.0 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.15.0.dev20210225-py3-none-any.whl (214.1 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.15.0.dev20210225.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.15.0.dev20210225.tar.gz
  • Upload date:
  • Size: 109.0 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-0.15.0.dev20210225.tar.gz
Algorithm Hash digest
SHA256 2e544187f9feb55502a145b0c0a0011cc0bc76f198bfecc94d0abac3420037e0
MD5 6fdaf91e11f192bb4f3df77f3968ded0
BLAKE2b-256 713594424376f3e26089477983e83c11579507eb64a74886461d5f83505a6106

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.15.0.dev20210225-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.15.0.dev20210225-py3-none-any.whl
Algorithm Hash digest
SHA256 373b8f6808bc5e3005c168c4c862d296150557b4304100a5a57eca6629c4a0b2
MD5 0e318af4b595336c5b275a558e94ed39
BLAKE2b-256 f62fde1bb7d13922753c3813e04b1d38230c71b403b0db53eb32cb11e2dbf5f7

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