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.dev20220216.tar.gz (63.2 kB view details)

Uploaded Source

Built Distribution

flwr_nightly-0.18.0.dev20220216-py3-none-any.whl (105.0 kB view details)

Uploaded Python 3

File details

Details for the file flwr-nightly-0.18.0.dev20220216.tar.gz.

File metadata

  • Download URL: flwr-nightly-0.18.0.dev20220216.tar.gz
  • Upload date:
  • Size: 63.2 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.dev20220216.tar.gz
Algorithm Hash digest
SHA256 59c7dc6453b3b1f65ee42038e5e28799b5dab6b4935b84f8a82cd7a11f153013
MD5 6a21892b0613aaa08f878aadae3cdd66
BLAKE2b-256 e5783609a31777fe59d1f854a68d9b2b122b73ce6181ca76428db077542a12ff

See more details on using hashes here.

File details

Details for the file flwr_nightly-0.18.0.dev20220216-py3-none-any.whl.

File metadata

File hashes

Hashes for flwr_nightly-0.18.0.dev20220216-py3-none-any.whl
Algorithm Hash digest
SHA256 73d9a50e9972767d5c047c8acd4f0fa3c56abc5585b8bd0b2a83f970859c7525
MD5 ee345c9926e947fa87484dbfbb3df1da
BLAKE2b-256 73c2e97632bdfe80b2b1275d2df6743c59834d7b3dc1dac332b4cbbd0e7d4cf6

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