Skip to main content

Flower - A Friendly Federated Learning Framework

Project description

Flower - A Friendly Federated Learning Framework

Flower Website

Website | Blog | Docs | Conference | Slack

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 built 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, Hugging Face Transformers, PyTorch Lightning, MXNet, scikit-learn, JAX, TFLite, 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!

Federated Learning Tutorial

Flower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated learning and how to implement them in Flower.

  1. An Introduction to Federated Learning

    Open In Colab (or open the Jupyter Notebook)

  2. Using Strategies in Federated Learning

    Open In Colab (or open the Jupyter Notebook)

  3. Building Strategies for Federated Learning

    --- coming soon ---

  4. Privacy and Security in Federated Learning

    --- coming soon ---

  5. Scaling Federated Learning

    --- coming soon ---

Documentation

Flower Docs:

Flower Baselines

Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:

Check the Flower documentation to learn more: Using Baselines

The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: Contributing Baselines

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:

Community

Flower is built by a wonderful community of researchers and engineers. Join Slack to meet them, contributions are welcome.

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-1.1.0.dev20220801.tar.gz (56.5 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-1.1.0.dev20220801-py3-none-any.whl (90.4 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-1.1.0.dev20220801.tar.gz.

File metadata

  • Download URL: flwr-nightly-1.1.0.dev20220801.tar.gz
  • Upload date:
  • Size: 56.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.14 CPython/3.7.12 Linux/5.15.0-1014-azure

File hashes

Hashes for flwr-nightly-1.1.0.dev20220801.tar.gz
Algorithm Hash digest
SHA256 9b649455a3c86f522dd43ed57fab68ffac28a840673ed5302edfcb940f418f86
MD5 6f7b08dfa7e14e7b200939591bafc192
BLAKE2b-256 d1455070470bbe6ee866c2795053539fce5b4d9a43c0d4bf19638c352e2bf1a6

See more details on using hashes here.

File details

Details for the file flwr_nightly-1.1.0.dev20220801-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-1.1.0.dev20220801-py3-none-any.whl
Algorithm Hash digest
SHA256 514b035eee433e79f4c0093dda0cb28ecb42c16e6977c0ab42dc6800c798a42f
MD5 f51d56689733521f46646d2382e117f4
BLAKE2b-256 d666f6b8f16dc6287aa9f8e68a1fb16fc5d733193bb6039f31b229e61f90fdb0

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