Fix broken baselines contributing link (#2541)

Co-authored-by: gfmota <[email protected]> Co-authored-by: Javier <[email protected]>
adap · Oct 30, 2023 · d58812a · d58812a
1 parent da9b326
commit d58812a
Showing 1 changed file with 49 additions and 50 deletions.
diff --git a/README.md b/README.md
@@ -23,22 +23,21 @@
 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
+- **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
+- **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
+- **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/)
+  [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
+- **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)!
@@ -58,11 +57,11 @@ Flower's goal is to make federated learning accessible to everyone. This series
 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-use-a-federated-learning-strategy-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-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-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))
-   
+
 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))
@@ -73,66 +72,66 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
 
 [![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.dev/docs):
-* [Installation](https://flower.dev/docs/framework/how-to-install-flower.html)
-* [Quickstart (TensorFlow)](https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html)
-* [Quickstart (PyTorch)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html)
-* [Quickstart (Hugging Face)](https://flower.dev/docs/framework/tutorial-quickstart-huggingface.html)
-* [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
-* [Quickstart (MXNet)](https://flower.dev/docs/framework/example-mxnet-walk-through.html)
-* [Quickstart (Pandas)](https://flower.dev/docs/framework/tutorial-quickstart-pandas.html)
-* [Quickstart (fastai)](https://flower.dev/docs/framework/tutorial-quickstart-fastai.html)
-* [Quickstart (JAX)](https://flower.dev/docs/framework/tutorial-quickstart-jax.html)
-* [Quickstart (scikit-learn)](https://flower.dev/docs/framework/tutorial-quickstart-scikitlearn.html)
-* [Quickstart (Android [TFLite])](https://flower.dev/docs/framework/tutorial-quickstart-android.html)
-* [Quickstart (iOS [CoreML])](https://flower.dev/docs/framework/tutorial-quickstart-ios.html)
+
+- [Installation](https://flower.dev/docs/framework/how-to-install-flower.html)
+- [Quickstart (TensorFlow)](https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html)
+- [Quickstart (PyTorch)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html)
+- [Quickstart (Hugging Face)](https://flower.dev/docs/framework/tutorial-quickstart-huggingface.html)
+- [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
+- [Quickstart (MXNet)](https://flower.dev/docs/framework/example-mxnet-walk-through.html)
+- [Quickstart (Pandas)](https://flower.dev/docs/framework/tutorial-quickstart-pandas.html)
+- [Quickstart (fastai)](https://flower.dev/docs/framework/tutorial-quickstart-fastai.html)
+- [Quickstart (JAX)](https://flower.dev/docs/framework/tutorial-quickstart-jax.html)
+- [Quickstart (scikit-learn)](https://flower.dev/docs/framework/tutorial-quickstart-scikitlearn.html)
+- [Quickstart (Android [TFLite])](https://flower.dev/docs/framework/tutorial-quickstart-android.html)
+- [Quickstart (iOS [CoreML])](https://flower.dev/docs/framework/tutorial-quickstart-ios.html)
 
 ## 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/flwr_baselines/publications/fedavg_mnist)
-* [FedProx](https://arxiv.org/abs/1812.06127):
-  * [MNIST](https://github.com/adap/flower/tree/main/baselines/fedprox/)
-* [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/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/flwr_baselines/publications/adaptive_federated_optimization)
+- [FedAvg](https://arxiv.org/abs/1602.05629):
+  - [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
+- [FedProx](https://arxiv.org/abs/1812.06127):
+  - [MNIST](https://github.com/adap/flower/tree/main/baselines/fedprox/)
+- [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/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/flwr_baselines/publications/adaptive_federated_optimization)
 
-Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/baselines/using-baselines.html)
+Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/baselines/how-to-use-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/baselines/contributing-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/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 (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 (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
-* [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)
+- [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 (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
+- [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)
 
 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)
-* [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))
+- [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)
+- [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
 
@@ -144,12 +143,12 @@ Flower is built by a wonderful community of researchers and engineers. [Join Sla
 
 ## Citation
 
-If you publish work that uses Flower, please cite Flower as follows: 
+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}, 
+  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}
 }