Metadata-Version: 2.1
Name: flwr
Version: 0.7.0
Summary: Flower - A Friendly Federated Learning Research Framework
Home-page: https://flower.dev
Author: The Flower Authors
Author-email: enquiries@flower.dev
License: UNKNOWN
Description: # Flower (`flwr`) - A Friendly Federated Learning Research Framework
        
        [![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/workflows/Build/badge.svg)
        
        Flower (`flwr`) is a research 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](https://pytorch.org),
          [TensorFlow](https://tensorflow.org), 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.
        
        ## Documentation
        
        * [Documentation](https://flower.dev)
          * [Quickstart](https://flower.dev/quickstart.html)
          * [Installation](https://flower.dev/installation.html)
        
        ## 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](https://flower.dev/examples.html)
        
        Available [examples](src/py/flwr_example):
        
        * [Quickstart: Keras & MNIST](src/py/flwr_example/quickstart)
        * [PyTorch & CIFAR-10](src/py/flwr_example/pytorch)
        * [PyTorch & ImageNet](src/py/flwr_example/pytorch_imagenet)
        * [TensorFlow & Fashion-MNIST](src/py/flwr_example/tensorflow)
        
        ## Flower Baselines
        
        *Coming soon* - curious minds can take a peek at [src/py/flwr_experimental/baseline](src/py/flwr_experimental/baseline).
        
        ## Flower Datasets
        
        *Coming soon* - curious minds can take a peek at [src/py/flwr_experimental/baseline/dataset](src/py/flwr_experimental/baseline/dataset).
        
        ## Contributing to Flower
        
        We welcome contributions. Please see [CONTRIBUTING.md](CONTRIBUTING.md) to get
        started!
        
Platform: UNKNOWN
Requires-Python: >=3.6.9,<4.0.0
Provides-Extra: baseline
Provides-Extra: examples-pytorch
Provides-Extra: examples-tensorflow
Provides-Extra: http-logger
Provides-Extra: ops
