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, 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-0.17.0.dev20210602.tar.gz (111.6 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.17.0.dev20210602-py3-none-any.whl (216.7 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.17.0.dev20210602.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.17.0.dev20210602.tar.gz
  • Upload date:
  • Size: 111.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.6 CPython/3.7.9 Linux/5.4.0-1047-azure

File hashes

Hashes for flwr-nightly-0.17.0.dev20210602.tar.gz
Algorithm Hash digest
SHA256 2379f50a8a2e92a8bb60b875bbea231850271aac68a13c2a0cc35850e47142f0
MD5 206bc5d55e261b1de7dd8e97216c8380
BLAKE2b-256 cba67df316c435cc48538b3a61b4fe3a9c19e65204e6a086bc75ca921c972bb5

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.17.0.dev20210602-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.17.0.dev20210602-py3-none-any.whl
Algorithm Hash digest
SHA256 06aaf76d73eff7f89de64bd68f4ea085d56ea88f1c1f81d22d370a20fb4a3fc5
MD5 f5988bf1807b284e7679151266096fd8
BLAKE2b-256 cbd95b7295e247c72e36eb3e2d2a6a8b64eed6e176355f15e1d5801b58c1baa3

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