Flower: A Friendly Federated Learning Framework

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Build

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.

What is Federated Learning?

(or open the Jupyter Notebook)
An Introduction to Federated Learning

(or open the Jupyter Notebook)
Using Strategies in Federated Learning

(or open the Jupyter Notebook)
Building Strategies for Federated Learning

(or open the Jupyter Notebook)
Custom Clients for Federated Learning

(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

(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:

Raspberry Pi & Nvidia Jetson Tutorial
PyTorch: From Centralized to Federated
Vertical FL
Federated Finetuning of OpenAI's Whisper
Federated Finetuning of Large Language Model
Federated Finetuning of a Vision Transformer
Advanced Flower with TensorFlow/Keras
Advanced Flower with PyTorch
Single-Machine Simulation of Federated Learning Systems (PyTorch) (Tensorflow)
Comprehensive Flower+XGBoost
Flower through Docker Compose and with Grafana dashboard
Flower with KaplanMeierFitter from the lifelines library
Sample Level Privacy with Opacus
Sample Level Privacy with TensorFlow-Privacy
[Flower with a Tabular Dataset] (https://github.com/adap/flower/tree/main/examples/fl-tabular)

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!

Metadata

Release files for flwr-nightly 1.10.0.dev20240620

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for flwr-nightly 1.10.0.dev20240620
File	Size	Uploaded
flwr_nightly-1.10.0.dev20240620.tar.gz	218.1 kB	Jun 20, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for flwr-nightly 1.10.0.dev20240620
File	Interpreter	ABI	Platform	Reset
flwr_nightly-1.10.0.dev20240620-py3-none-any.whl 391.6 kB Jun 20, 2024	Python 3	none	any	Details

Total release size: 609.7 kB

Release files / flwr_nightly-1.10.0.dev20240620.tar.gz

Download URL	flwr_nightly-1.10.0.dev20240620.tar.gz
Size	218.1 kB
Tags	Source
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BLAKE2b-256 checksum How to use checksums	`b29620302980241b519ef5ea9b9db0219717532cc3e3093625ad4bc915418b9e`
Upload date	Jun 20, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.7.1 CPython/3.8.18 Linux/6.5.0-1022-azure`

Release files / flwr_nightly-1.10.0.dev20240620-py3-none-any.whl

Download URL	flwr_nightly-1.10.0.dev20240620-py3-none-any.whl
Size	391.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`6e76a8644e8d09920b00a4f6d911bb4de5fbb84d520a32a938c2066361349325`
BLAKE2b-256 checksum How to use checksums	`b86ef078bc758e21e73a599aa5474966b273ab9d32addb37a0260c54af94f11f`
Upload date	Jun 20, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.7.1 CPython/3.8.18 Linux/6.5.0-1022-azure`