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KarhouTam committed Oct 3, 2024
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118 changes: 59 additions & 59 deletions README.md
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<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)
[![GitHub license](https://img.shields.io/github/license/adap/flower)](LICENSE)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](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)
Expand Down Expand Up @@ -48,29 +48,29 @@ Flower's goal is to make federated learning accessible to everyone. This series

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))
[![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](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))
[![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](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))
[![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](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))
[![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](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))
[![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](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))
[![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](examples/flower-in-30-minutes/tutorial.ipynb))

## Documentation

Expand All @@ -92,27 +92,27 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit

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)
- [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)
- [DASHA](baselines/dasha/README.md)
- [DepthFL](baselines/depthfl/README.md)
- [FedBN](baselines/fedbn/README.md)
- [FedMeta](baselines/fedmeta/README.md)
- [FedMLB](baselines/fedmlb/README.md)
- [FedPer](baselines/fedper/README.md)
- [FedProx](baselines/fedprox/README.md)
- [FedNova](baselines/fednova/README.md)
- [HeteroFL](baselines/heterofl/README.md)
- [FedAvgM](baselines/fedavgm/README.md)
- [FedStar](baselines/fedstar/README.md)
- [FedWav2vec2](baselines/fedwav2vec2/README.md)
- [FjORD](baselines/fjord/README.md)
- [MOON](baselines/moon/README.md)
- [niid-Bench](baselines/niid_bench/README.md)
- [TAMUNA](baselines/tamuna/README.md)
- [FedVSSL](baselines/fedvssl/README.md)
- [FedXGBoost](baselines/hfedxgboost/README.md)
- [FedPara](baselines/fedpara/README.md)
- [FedAvg](baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/README.md)
- [FedOpt](baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/README.md)

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)
Expand All @@ -124,36 +124,36 @@ Several code examples show different usage scenarios of Flower (in combination w

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)
- [Quickstart (TensorFlow)](examples/quickstart-tensorflow/README.md)
- [Quickstart (PyTorch)](examples/quickstart-pytorch/README.md)
- [Quickstart (Hugging Face)](examples/quickstart-huggingface/README.md)
- [Quickstart (PyTorch Lightning)](examples/quickstart-pytorch-lightning/README.md)
- [Quickstart (fastai)](examples/quickstart-fastai/README.md)
- [Quickstart (Pandas)](examples/quickstart-pandas/README.md)
- [Quickstart (JAX)](examples/quickstart-jax/README.md)
- [Quickstart (MONAI)](examples/quickstart-monai/README.md)
- [Quickstart (scikit-learn)](examples/sklearn-logreg-mnist/README.md)
- [Quickstart (Android [TFLite])](examples/android/README.md)
- [Quickstart (iOS [CoreML])](examples/ios/README.md)
- [Quickstart (MLX)](examples/quickstart-mlx/README.md)
- [Quickstart (XGBoost)](examples/xgboost-quickstart/README.md)

Other [examples](examples):

- [Raspberry Pi & Nvidia Jetson Tutorial](examples/embedded-devices/README.md)
- [PyTorch: From Centralized to Federated](examples/pytorch-from-centralized-to-federated/README.md)
- [Vertical FL](examples/vertical-fl/README.md)
- [Federated Finetuning of OpenAI's Whisper](examples/whisper-federated-finetuning/README.md)
- [Federated Finetuning of Large Language Model](examples/flowertune-llm/README.md)
- [Federated Finetuning of a Vision Transformer](examples/flowertune-vit/README.md)
- [Advanced Flower with TensorFlow/Keras](examples/advanced-tensorflow/README.md)
- [Advanced Flower with PyTorch](examples/advanced-pytorch/README.md)
- [Comprehensive Flower+XGBoost](examples/xgboost-comprehensive/README.md)
- [Flower through Docker Compose and with Grafana dashboard](examples/flower-via-docker-compose/README.md)
- [Flower with KaplanMeierFitter from the lifelines library](examples/federated-kaplan-meier-fitter/README.md)
- [Sample Level Privacy with Opacus](examples/opacus/README.md)
- [Sample Level Privacy with TensorFlow-Privacy](examples/tensorflow-privacy/README.md)
- [Flower with a Tabular Dataset](examples/fl-tabular/README.md)

## Community

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> [!NOTE]
> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [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), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist).
> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](flwr_baselines/flwr_baselines/publications/leaf/femnist).

## Structure
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# Flower Datasets

[![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)
[![GitHub license](https://img.shields.io/github/license/adap/flower)](../LICENSE)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](../CONTRIBUTING.md)
![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
![Downloads](https://pepy.tech/badge/flwr-datasets)
[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack)
Expand All @@ -10,7 +10,7 @@ Flower Datasets (`flwr-datasets`) is a library to quickly and easily create data


> [!TIP]
> For complete documentation that includes API docs, how-to guides and tutorials, please visit the [Flower Datasets Documentation](https://flower.ai/docs/datasets/) and for full FL example see the [Flower Examples page](https://github.com/adap/flower/tree/main/examples).
> For complete documentation that includes API docs, how-to guides and tutorials, please visit the [Flower Datasets Documentation](https://flower.ai/docs/datasets/) and for full FL example see the [Flower Examples page](../examples).
## Installation

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