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https://github.com/adap/flower

Flower: A Friendly Federated AI Framework
https://github.com/adap/flower

ai android artificial-intelligence cpp deep-learning federated-analytics federated-learning federated-learning-framework fleet-intelligence fleet-learning flower framework grpc ios machine-learning python pytorch raspberry-pi scikit-learn tensorflow

Last synced: 11 days ago
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Flower: A Friendly Federated AI Framework

Host: GitHub
URL: https://github.com/adap/flower
Owner: adap
License: apache-2.0
Created: 2020-02-17T11:51:29.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2024-10-24T20:11:07.000Z (15 days ago)
Last Synced: 2024-10-25T05:08:32.145Z (14 days ago)
Topics: ai, android, artificial-intelligence, cpp, deep-learning, federated-analytics, federated-learning, federated-learning-framework, fleet-intelligence, fleet-learning, flower, framework, grpc, ios, machine-learning, python, pytorch, raspberry-pi, scikit-learn, tensorflow
Language: Python
Homepage: https://flower.ai
Size: 120 MB
Stars: 5,026
Watchers: 42
Forks: 862
Open Issues: 528
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

awesome-llmops - Flower - square) | (Federated ML / Profiling)
Aweome-Heathcare-Federated-Learning - [Code
awesome-machine-learning-resources - **[Library
awesome-production-machine-learning - Flower - Flower is a Federated Learning Framework with a unified approach. It enables the federation of any ML workload, with any ML framework, and any programming language. (Privacy and Security)
awesome-ray - Flower - node GPU cluster. (Models and Projects / distributed computing)
awesome-flower-list - Flower Repository
grpc-protos - adap/flower
StarryDivineSky - adap/flower - learn 、 JAX 、 TFLite 、 MONAI 、 fastai 、 MLX 、 XGBoost 、 Pandas进行联合分析，甚至原始NumPy进行联合分析喜欢手动计算梯度的用户。可以理解：Flower 的编写考虑到了可维护性。鼓励社区阅读代码库并为代码库做出贡献。 (其他_机器学习与深度学习)

README

        # Flower: A Friendly Federated Learning Framework



  

    

  





    Website |

    Blog |

    Docs |

    Conference |

    Slack

    





[![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)

![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)

[![Downloads](https://static.pepy.tech/badge/flwr)](https://pepy.tech/project/flwr)

[![Docker Hub](https://img.shields.io/badge/Docker%20Hub-flwr-blue)](https://hub.docker.com/u/flwr)

[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-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](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [MONAI](https://docs.monai.io/en/latest/index.html), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)

  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](https://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.

0. **What is Federated Learning?**

   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-what-is-federated-learning.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-what-is-federated-learning.ipynb))

1. **An Introduction to Federated Learning**

   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb))

2. **Using Strategies in Federated Learning**

   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb))

3. **Building Strategies for Federated Learning**

   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb))

4. **Custom Clients for Federated Learning**

   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-customize-the-client-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-customize-the-client-pytorch.ipynb))

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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/examples/flower-in-30-minutes/tutorial.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/examples/flower-in-30-minutes/tutorial.ipynb))

## Documentation

[Flower Docs](https://flower.ai/docs):

- [Installation](https://flower.ai/docs/framework/how-to-install-flower.html)

- [Quickstart (TensorFlow)](https://flower.ai/docs/framework/tutorial-quickstart-tensorflow.html)

- [Quickstart (PyTorch)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html)

- [Quickstart (Hugging Face)](https://flower.ai/docs/framework/tutorial-quickstart-huggingface.html)

- [Quickstart (PyTorch Lightning)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch-lightning.html)

- [Quickstart (Pandas)](https://flower.ai/docs/framework/tutorial-quickstart-pandas.html)

- [Quickstart (fastai)](https://flower.ai/docs/framework/tutorial-quickstart-fastai.html)

- [Quickstart (JAX)](https://flower.ai/docs/framework/tutorial-quickstart-jax.html)

- [Quickstart (scikit-learn)](https://flower.ai/docs/framework/tutorial-quickstart-scikitlearn.html)

- [Quickstart (Android [TFLite])](https://flower.ai/docs/framework/tutorial-quickstart-android.html)

- [Quickstart (iOS [CoreML])](https://flower.ai/docs/framework/tutorial-quickstart-ios.html)

## 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!

- [DASHA](https://github.com/adap/flower/tree/main/baselines/dasha)

- [DepthFL](https://github.com/adap/flower/tree/main/baselines/depthfl)

- [FedBN](https://github.com/adap/flower/tree/main/baselines/fedbn)

- [FedMeta](https://github.com/adap/flower/tree/main/baselines/fedmeta)

- [FedMLB](https://github.com/adap/flower/tree/main/baselines/fedmlb)

- [FedPer](https://github.com/adap/flower/tree/main/baselines/fedper)

- [FedProx](https://github.com/adap/flower/tree/main/baselines/fedprox)

- [FedNova](https://github.com/adap/flower/tree/main/baselines/fednova)

- [HeteroFL](https://github.com/adap/flower/tree/main/baselines/heterofl)

- [FedAvgM](https://github.com/adap/flower/tree/main/baselines/fedavgm)

- [FedRep](https://github.com/adap/flower/tree/main/baselines/fedrep)

- [FedStar](https://github.com/adap/flower/tree/main/baselines/fedstar)

- [FedWav2vec2](https://github.com/adap/flower/tree/main/baselines/fedwav2vec2)

- [FjORD](https://github.com/adap/flower/tree/main/baselines/fjord)

- [MOON](https://github.com/adap/flower/tree/main/baselines/moon)

- [niid-Bench](https://github.com/adap/flower/tree/main/baselines/niid_bench)

- [TAMUNA](https://github.com/adap/flower/tree/main/baselines/tamuna)

- [FedVSSL](https://github.com/adap/flower/tree/main/baselines/fedvssl)

- [FedXGBoost](https://github.com/adap/flower/tree/main/baselines/hfedxgboost)

- [FedPara](https://github.com/adap/flower/tree/main/baselines/fedpara)

- [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)

- [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)

Please refer to the [Flower Baselines Documentation](https://flower.ai/docs/baselines/) for a detailed categorization of baselines and for additional info including:

* [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)

* [How to contribute a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)

## 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:

- [Quickstart (TensorFlow)](https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow)

- [Quickstart (PyTorch)](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch)

- [Quickstart (Hugging Face)](https://github.com/adap/flower/tree/main/examples/quickstart-huggingface)

- [Quickstart (PyTorch Lightning)](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning)

- [Quickstart (fastai)](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)

- [Quickstart (Pandas)](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)

- [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart-jax)

- [Quickstart (MONAI)](https://github.com/adap/flower/tree/main/examples/quickstart-monai)

- [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)

- [Quickstart (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)

- [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)

- [Quickstart (MLX)](https://github.com/adap/flower/tree/main/examples/quickstart-mlx)

- [Quickstart (XGBoost)](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)

Other [examples](https://github.com/adap/flower/tree/main/examples):

- [Raspberry Pi & Nvidia Jetson Tutorial](https://github.com/adap/flower/tree/main/examples/embedded-devices)

- [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated)

- [Vertical FL](https://github.com/adap/flower/tree/main/examples/vertical-fl)

- [Federated Finetuning of OpenAI's Whisper](https://github.com/adap/flower/tree/main/examples/whisper-federated-finetuning)

- [Federated Finetuning of Large Language Model](https://github.com/adap/flower/tree/main/examples/flowertune-llm)

- [Federated Finetuning of a Vision Transformer](https://github.com/adap/flower/tree/main/examples/flowertune-vit)

- [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced-tensorflow)

- [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)

- [Comprehensive Flower+XGBoost](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)

- [Flower through Docker Compose and with Grafana dashboard](https://github.com/adap/flower/tree/main/examples/flower-via-docker-compose)

- [Flower with KaplanMeierFitter from the lifelines library](https://github.com/adap/flower/tree/main/examples/federated-kaplan-meier-fitter)

- [Sample Level Privacy with Opacus](https://github.com/adap/flower/tree/main/examples/opacus)

- [Sample Level Privacy with TensorFlow-Privacy](https://github.com/adap/flower/tree/main/examples/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](https://flower.ai/join-slack) to meet them, [contributions](#contributing-to-flower) are welcome.



  



## Citation

If you publish work that uses Flower, please cite Flower as follows:

```bibtex

@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](CONTRIBUTING.md) to get started!