%global _empty_manifest_terminate_build 0
Name: python-flwr
Version: 1.4.0
Release: 1
Summary: Flower: A Friendly Federated Learning Framework
License: Apache-2.0
URL: https://flower.dev
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e9/91/c93ccd0a61c91315cbc57c100adfe08533ec4ed19feebbb80cb3f5747740/flwr-1.4.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-grpcio
Requires: python3-protobuf
Requires: python3-importlib-metadata
Requires: python3-iterators
Requires: python3-ray[default]
Requires: python3-requests
Requires: python3-fastapi
Requires: python3-starlette
Requires: python3-uvicorn[standard]
%description
# Flower: A Friendly Federated Learning Framework
Website |
Blog |
Docs |
Conference |
Slack
[](https://github.com/adap/flower/blob/main/LICENSE)
[](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
[](https://flower.dev/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/), [MXNet](https://mxnet.apache.org/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [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.dev](https://flower.dev)!
## 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?**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb))
1. **An Introduction to Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb))
2. **Using Strategies in Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb))
3. **Building Strategies for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb))
4. **Custom Clients for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb))
Stay tuned, more tutorials are coming soon. Topics include **Privacy and Security in Federated Learning**, and **Scaling Federated Learning**.
## Documentation
[Flower Docs](https://flower.dev/docs):
* [Installation](https://flower.dev/docs/installation.html)
* [Quickstart (TensorFlow)](https://flower.dev/docs/quickstart-tensorflow.html)
* [Quickstart (PyTorch)](https://flower.dev/docs/quickstart-pytorch.html)
* [Quickstart (Hugging Face [code example])](https://flower.dev/docs/quickstart-huggingface.html)
* [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/quickstart-pytorch-lightning.html)
* [Quickstart (MXNet)](https://flower.dev/docs/example-mxnet-walk-through.html)
* [Quickstart (Pandas)](https://flower.dev/docs/quickstart-pandas.html)
* [Quickstart (fastai)](https://flower.dev/docs/quickstart-fastai.html)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android [code example])](https://github.com/adap/flower/tree/main/examples/android)
## Flower Baselines
Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:
* [FedAvg](https://arxiv.org/abs/1602.05629):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedavg_mnist)
* [FedProx](https://arxiv.org/abs/1812.06127):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedprox_mnist)
* [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623):
* [Convergence Rate](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedbn/convergence_rate)
* [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
* [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/adaptive_federated_optimization)
Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/using-baselines.html)
The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: [Contributing Baselines](https://flower.dev/docs/contributing-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 (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart_mxnet)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android)](https://github.com/adap/flower/tree/main/examples/android)
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)
* [Android & TFLite](https://github.com/adap/flower/tree/main/examples/android)
* [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated)
* [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet_from_centralized_to_federated)
* [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)
* Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
## Community
Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.dev/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!
%package -n python3-flwr
Summary: Flower: A Friendly Federated Learning Framework
Provides: python-flwr
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-flwr
# Flower: A Friendly Federated Learning Framework
Website |
Blog |
Docs |
Conference |
Slack
[](https://github.com/adap/flower/blob/main/LICENSE)
[](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
[](https://flower.dev/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/), [MXNet](https://mxnet.apache.org/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [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.dev](https://flower.dev)!
## 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?**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb))
1. **An Introduction to Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb))
2. **Using Strategies in Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb))
3. **Building Strategies for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb))
4. **Custom Clients for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb))
Stay tuned, more tutorials are coming soon. Topics include **Privacy and Security in Federated Learning**, and **Scaling Federated Learning**.
## Documentation
[Flower Docs](https://flower.dev/docs):
* [Installation](https://flower.dev/docs/installation.html)
* [Quickstart (TensorFlow)](https://flower.dev/docs/quickstart-tensorflow.html)
* [Quickstart (PyTorch)](https://flower.dev/docs/quickstart-pytorch.html)
* [Quickstart (Hugging Face [code example])](https://flower.dev/docs/quickstart-huggingface.html)
* [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/quickstart-pytorch-lightning.html)
* [Quickstart (MXNet)](https://flower.dev/docs/example-mxnet-walk-through.html)
* [Quickstart (Pandas)](https://flower.dev/docs/quickstart-pandas.html)
* [Quickstart (fastai)](https://flower.dev/docs/quickstart-fastai.html)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android [code example])](https://github.com/adap/flower/tree/main/examples/android)
## Flower Baselines
Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:
* [FedAvg](https://arxiv.org/abs/1602.05629):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedavg_mnist)
* [FedProx](https://arxiv.org/abs/1812.06127):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedprox_mnist)
* [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623):
* [Convergence Rate](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedbn/convergence_rate)
* [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
* [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/adaptive_federated_optimization)
Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/using-baselines.html)
The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: [Contributing Baselines](https://flower.dev/docs/contributing-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 (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart_mxnet)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android)](https://github.com/adap/flower/tree/main/examples/android)
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)
* [Android & TFLite](https://github.com/adap/flower/tree/main/examples/android)
* [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated)
* [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet_from_centralized_to_federated)
* [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)
* Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
## Community
Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.dev/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!
%package help
Summary: Development documents and examples for flwr
Provides: python3-flwr-doc
%description help
# Flower: A Friendly Federated Learning Framework
Website |
Blog |
Docs |
Conference |
Slack
[](https://github.com/adap/flower/blob/main/LICENSE)
[](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
[](https://flower.dev/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/), [MXNet](https://mxnet.apache.org/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [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.dev](https://flower.dev)!
## 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?**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-0-What-is-FL.ipynb))
1. **An Introduction to Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb))
2. **Using Strategies in Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb))
3. **Building Strategies for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb))
4. **Custom Clients for Federated Learning**
[](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.ipynb))
Stay tuned, more tutorials are coming soon. Topics include **Privacy and Security in Federated Learning**, and **Scaling Federated Learning**.
## Documentation
[Flower Docs](https://flower.dev/docs):
* [Installation](https://flower.dev/docs/installation.html)
* [Quickstart (TensorFlow)](https://flower.dev/docs/quickstart-tensorflow.html)
* [Quickstart (PyTorch)](https://flower.dev/docs/quickstart-pytorch.html)
* [Quickstart (Hugging Face [code example])](https://flower.dev/docs/quickstart-huggingface.html)
* [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/quickstart-pytorch-lightning.html)
* [Quickstart (MXNet)](https://flower.dev/docs/example-mxnet-walk-through.html)
* [Quickstart (Pandas)](https://flower.dev/docs/quickstart-pandas.html)
* [Quickstart (fastai)](https://flower.dev/docs/quickstart-fastai.html)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android [code example])](https://github.com/adap/flower/tree/main/examples/android)
## Flower Baselines
Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:
* [FedAvg](https://arxiv.org/abs/1602.05629):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedavg_mnist)
* [FedProx](https://arxiv.org/abs/1812.06127):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedprox_mnist)
* [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623):
* [Convergence Rate](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedbn/convergence_rate)
* [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
* [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/adaptive_federated_optimization)
Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/using-baselines.html)
The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: [Contributing Baselines](https://flower.dev/docs/contributing-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 (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart_mxnet)
* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
* [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
* [Quickstart (TFLite on Android)](https://github.com/adap/flower/tree/main/examples/android)
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)
* [Android & TFLite](https://github.com/adap/flower/tree/main/examples/android)
* [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated)
* [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet_from_centralized_to_federated)
* [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)
* Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
## Community
Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.dev/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!
%prep
%autosetup -n flwr-1.4.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-flwr -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.4.0-1
- Package Spec generated