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, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, 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.ai!

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 projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!

Please refer to the Flower Baselines Documentation for a detailed categorization of baselines and for additional info including:

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-1.9.0.dev20240429.tar.gz (199.4 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-1.9.0.dev20240429-py3-none-any.whl (346.0 kB view details)

Uploaded Python 3

File details

Details for the file flwr_nightly-1.9.0.dev20240429.tar.gz.

File metadata

  • Download URL: flwr_nightly-1.9.0.dev20240429.tar.gz
  • Upload date:
  • Size: 199.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.1 CPython/3.8.18 Linux/6.5.0-1018-azure

File hashes

Hashes for flwr_nightly-1.9.0.dev20240429.tar.gz
Algorithm Hash digest
SHA256 09eae7c4518b7bfccd7f076d4f5a36a76c2d9532dad6b4638fc81cf57f98eb44
MD5 00d62b18ffd5aaa36fac5acaa59872ad
BLAKE2b-256 f4bf16159f96fbd149e26a9ad282097b901cf94cd519c5314c228bc5c6e528ad

See more details on using hashes here.

File details

Details for the file flwr_nightly-1.9.0.dev20240429-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-1.9.0.dev20240429-py3-none-any.whl
Algorithm Hash digest
SHA256 07579c3d11f85509cd8a3ddd20465d711a41eda22cef40330eae61af5cb6514a
MD5 a228ff8e366dcf2cd8eaf65131114b21
BLAKE2b-256 cc4fe8ff96592b251aa02f5b456e6ac4e7d475c45ce836f5580918f58abdbc92

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