docs(framework) Improve URLs orientation for better user experience

README.md

@@ -14,8 +14,8 @@
     <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)
@@ -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
 
@@ -92,28 +92,28 @@ 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)
-- [FedRep](https://github.com/adap/flower/tree/main/baselines/fedrep)
-- [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)
+- [FedRep](baselines/fedrep/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)
@@ -125,36 +125,36 @@ 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
 

baselines/README.md

@@ -2,7 +2,7 @@
 
 
 > [!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/README.md), [FedOpt](flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/README.md), and [LEAF-FEMNIST](flwr_baselines/flwr_baselines/publications/leaf/femnist/README.md).
 
 
 ## Structure

datasets/README.md

@@ -1,7 +1,7 @@
 # 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)
@@ -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
 

examples/advanced-pytorch/README.md

@@ -7,9 +7,9 @@ framework: [torch, torchvision]
 # Federated Learning with PyTorch and Flower (Advanced Example)
 
 > \[!TIP\]
-> This example shows intermediate and advanced functionality of Flower. It you are new to Flower, it is recommended to start from the [quickstart-pytorch](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) example or the [quickstart PyTorch tutorial](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html).
+> This example shows intermediate and advanced functionality of Flower. It you are new to Flower, it is recommended to start from the [quickstart-pytorch](../quickstart-pytorch/README.md) example or the [quickstart PyTorch tutorial](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html).
 
-This example shows how to extend your `ClientApp` and `ServerApp` capabilities compared to what's shown in the [`quickstart-pytorch`](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) example. In particular, it will show how the `ClientApp`'s state (and object of type [RecordSet](https://flower.ai/docs/framework/ref-api/flwr.common.RecordSet.html)) can be used to enable stateful clients, facilitating the design of personalized federated learning strategies, among others. The `ServerApp` in this example makes use of a custom strategy derived from the built-in [FedAvg](https://flower.ai/docs/framework/ref-api/flwr.server.strategy.FedAvg.html). In addition, it will also showcase how to:
+This example shows how to extend your `ClientApp` and `ServerApp` capabilities compared to what's shown in the [`quickstart-pytorch`](../quickstart-pytorch/README.md) example. In particular, it will show how the `ClientApp`'s state (and object of type [RecordSet](https://flower.ai/docs/framework/ref-api/flwr.common.RecordSet.html)) can be used to enable stateful clients, facilitating the design of personalized federated learning strategies, among others. The `ServerApp` in this example makes use of a custom strategy derived from the built-in [FedAvg](https://flower.ai/docs/framework/ref-api/flwr.server.strategy.FedAvg.html). In addition, it will also showcase how to:
 
 1. Save model checkpoints
 2. Save the metrics available at the strategy (e.g. accuracies, losses)

examples/app-pytorch/README.md

@@ -7,7 +7,7 @@ framework: [torch, torchvision]
 # Flower App (PyTorch) 🧪
 
 > 🧪 = This example covers experimental features that might change in future versions of Flower
-> Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
+> Please consult the regular PyTorch code examples ([quickstart](../quickstart-pytorch/README.md), [advanced](../advanced-pytorch/README.md)) to learn how to use Flower with PyTorch.
 
 The following steps describe how to start a long-running Flower server (SuperLink) and then run a Flower App (consisting of a `ClientApp` and a `ServerApp`).
 

examples/custom-mods/README.md

@@ -7,7 +7,7 @@ framework: [wandb, tensorboard]
 # Using custom mods 🧪
 
 > 🧪 = This example covers experimental features that might change in future versions of Flower
-> Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
+> Please consult the regular PyTorch code examples ([quickstart](../quickstart-pytorch/README.md), [advanced](../advanced-pytorch/README.md)) to learn how to use Flower with PyTorch.
 
 The following steps describe how to write custom Flower Mods and use them in a simple example.