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, 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!

Documentation

Flower Docs:

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 / Datasets

Experimental - curious minds can take a peek at baselines.

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-0.18.0.dev20220208.tar.gz (61.9 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.18.0.dev20220208-py3-none-any.whl (103.6 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.18.0.dev20220208.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.18.0.dev20220208.tar.gz
  • Upload date:
  • Size: 61.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.12 CPython/3.7.12 Linux/5.11.0-1028-azure

File hashes

Hashes for flwr-nightly-0.18.0.dev20220208.tar.gz
Algorithm Hash digest
SHA256 38d8e6c67390b0f45d11d6392c0116364ea2a8da6d9f7686e01febda2f2344ca
MD5 4aec9bb1f2ceaff55dfaaf11981b5ffc
BLAKE2b-256 b884d8adab70df7255a4d39526d0bad1c4bb9e0437b71f853d69e3ba98479971

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.18.0.dev20220208-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.18.0.dev20220208-py3-none-any.whl
Algorithm Hash digest
SHA256 cd1d960eeda838aa4c9396731866500667949545275db52f358481695061a52f
MD5 5710551b952fda4e03478350c005612b
BLAKE2b-256 2a5dda856c16e3df1b58de99b7db4576b2907222a1e649ee8ecd0b6c789f1b27

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