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 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, 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-1.0.0.dev20220617.tar.gz (65.6 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-1.0.0.dev20220617-py3-none-any.whl (107.0 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-1.0.0.dev20220617.tar.gz.

File metadata

  • Download URL: flwr-nightly-1.0.0.dev20220617.tar.gz
  • Upload date:
  • Size: 65.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.13 CPython/3.7.12 Linux/5.13.0-1029-azure

File hashes

Hashes for flwr-nightly-1.0.0.dev20220617.tar.gz
Algorithm Hash digest
SHA256 5b71ebef07ef402e63d58717b14a9e43b58b1ae79fea8a9c7f6a6e5c607c666a
MD5 8bce04462758ff640c09a1899c81a45f
BLAKE2b-256 4f1cbc29b4a36d68c0cd31fd0babdabd37dde115a3e7af14b41fd7fc138dd7eb

See more details on using hashes here.

File details

Details for the file flwr_nightly-1.0.0.dev20220617-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-1.0.0.dev20220617-py3-none-any.whl
Algorithm Hash digest
SHA256 542de33a112960cde397c4c0b58d6164450b4cb0d448e9ca711e808942b5c9bb
MD5 0da5a38fb6968e4ba955e29fb3c99185
BLAKE2b-256 4a904c64358a45d043b9c9c615339b8bbc0777ff20d90cdf53b33e296948d631

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