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-0.13.0.dev20201229.tar.gz (98.4 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.13.0.dev20201229-py3-none-any.whl (199.3 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.13.0.dev20201229.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.13.0.dev20201229.tar.gz
  • Upload date:
  • Size: 98.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.4 CPython/3.7.9 Linux/5.4.0-1032-azure

File hashes

Hashes for flwr-nightly-0.13.0.dev20201229.tar.gz
Algorithm Hash digest
SHA256 e992bbee62584bc4b3e65a5e5987b36c13c85f1d7a52314dc2f4faf89168ac6f
MD5 8f5aaf15c63a6806bf479dacf0b369a1
BLAKE2b-256 d367ace41df8f994a39ef4c6dcdf69a1118df911d8123bf1f99af97bc50a8fcb

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.13.0.dev20201229-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.13.0.dev20201229-py3-none-any.whl
Algorithm Hash digest
SHA256 4ac36a89cf52394303023f23a731dd81ffeab3e79b6f33d08af7ffcf72dd9ce0
MD5 d28718f842553789a062430891600614
BLAKE2b-256 85007feb373f81f054fc84920c0c4d23eab8f7e42a279e9adce5ebead117cfd3

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