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+/flwr-1.4.0.tar.gz
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+%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
+
+<p align="center">
+  <a href="https://flower.dev/">
+    <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  </a>
+</p>
+<p align="center">
+    <a href="https://flower.dev/">Website</a> |
+    <a href="https://flower.dev/blog">Blog</a> |
+    <a href="https://flower.dev/docs/">Docs</a> |
+    <a href="https://flower.dev/conf/flower-summit-2022">Conference</a> |
+    <a href="https://flower.dev/join-slack">Slack</a>
+    <br /><br />
+</p>
+
+[![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/flower.yml/badge.svg)
+![Downloads](https://pepy.tech/badge/flwr)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](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?**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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.
+
+<a href="https://github.com/adap/flower/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=adap/flower" />
+</a>
+
+## 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
+
+<p align="center">
+  <a href="https://flower.dev/">
+    <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  </a>
+</p>
+<p align="center">
+    <a href="https://flower.dev/">Website</a> |
+    <a href="https://flower.dev/blog">Blog</a> |
+    <a href="https://flower.dev/docs/">Docs</a> |
+    <a href="https://flower.dev/conf/flower-summit-2022">Conference</a> |
+    <a href="https://flower.dev/join-slack">Slack</a>
+    <br /><br />
+</p>
+
+[![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/flower.yml/badge.svg)
+![Downloads](https://pepy.tech/badge/flwr)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](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?**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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.
+
+<a href="https://github.com/adap/flower/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=adap/flower" />
+</a>
+
+## 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
+
+<p align="center">
+  <a href="https://flower.dev/">
+    <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  </a>
+</p>
+<p align="center">
+    <a href="https://flower.dev/">Website</a> |
+    <a href="https://flower.dev/blog">Blog</a> |
+    <a href="https://flower.dev/docs/">Docs</a> |
+    <a href="https://flower.dev/conf/flower-summit-2022">Conference</a> |
+    <a href="https://flower.dev/join-slack">Slack</a>
+    <br /><br />
+</p>
+
+[![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/flower.yml/badge.svg)
+![Downloads](https://pepy.tech/badge/flwr)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](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?**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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**
+
+   [![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/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.
+
+<a href="https://github.com/adap/flower/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=adap/flower" />
+</a>
+
+## 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 <Python_Bot@openeuler.org> - 1.4.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..798470d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c21d326150950924eee68f9d707d5f06  flwr-1.4.0.tar.gz