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 University 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, fastai, Pandas for federated analytics, 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. What is Federated Learning?

    Open in Colab (or open the Jupyter Notebook)

  2. An Introduction to Federated Learning

    Open in Colab (or open the Jupyter Notebook)

  3. Using Strategies in Federated Learning

    Open in Colab (or open the Jupyter Notebook)

  4. Building Strategies for Federated Learning

    Open in Colab (or open the Jupyter Notebook)

  5. Custom Clients for Federated Learning

    Open in Colab (or open the Jupyter Notebook)

Stay tuned, more tutorials are coming soon. Topics include Privacy and Security in Federated Learning, and Scaling Federated Learning.

30-Minute Federated Learning Tutorial

Open in Colab (or open the Jupyter Notebook)

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

Several code examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow).

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 Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Kwing, Hei Li and Parcollet, Titouan and Gusmão, Pedro PB de 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!

Project details


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-1.6.0.dev20231016.tar.gz (117.4 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-1.6.0.dev20231016-py3-none-any.whl (204.1 kB view details)

Uploaded Python 3

File details

Details for the file flwr_nightly-1.6.0.dev20231016.tar.gz.

File metadata

  • Download URL: flwr_nightly-1.6.0.dev20231016.tar.gz
  • Upload date:
  • Size: 117.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.5.1 CPython/3.8.18 Linux/6.2.0-1012-azure

File hashes

Hashes for flwr_nightly-1.6.0.dev20231016.tar.gz
Algorithm Hash digest
SHA256 41462c604bf023b9155a523dd34fd4a83344c70695b6981ad27a4e3afe184968
MD5 1acebe7bfbf32a5279cd4b6757321293
BLAKE2b-256 1c588e01328066dfc6ceb6b019b3d6fd028759fe053140d2e8f073e508867c0b

See more details on using hashes here.

File details

Details for the file flwr_nightly-1.6.0.dev20231016-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-1.6.0.dev20231016-py3-none-any.whl
Algorithm Hash digest
SHA256 0704e752b64fd27271a2d40c1a01d623f0e0ce10f0e9bad881950d7dd6a6f5f2
MD5 8d8ee270e4267317967512a55f78dc7b
BLAKE2b-256 b599f074cf56583b0183c8b57f3a76c4dc2067ebfc29630c901c97f88f1cba91

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