NVIDIA · chesterxgchen · Dec 25, 2025 · Dec 25, 2025 · Dec 26, 2025 · Dec 26, 2025
diff --git a/docs/flare_overview.rst b/docs/flare_overview.rst
diff --git a/docs/flare_system_architecure.rst b/docs/flare_system_architecure.rst
diff --git a/docs/hello-world/hello-lightning/index.rst b/docs/hello-world/hello-lightning/index.rst
@@ -1,5 +1,6 @@
+.. _hello_lightning:
 
-Hello Pytorch Lightning
+Hello PyTorch Lightning
 =======================
 
 This example demonstrates how to use NVIDIA FLARE with PyTorch lightning to train an image classifier using

diff --git a/docs/hello-world/hello-pt/index.rst b/docs/hello-world/hello-pt/index.rst
@@ -1,3 +1,5 @@
+.. _hello_pt:
+
 Hello PyTorch
 =============
 

diff --git a/docs/hello-world/hello-tabular-stats/index.rst b/docs/hello-world/hello-tabular-stats/index.rst
@@ -1,3 +1,5 @@
+.. _hello_tabular_stats:
+
 Tabular Data Federated Statistics
 =================================
 
@@ -91,7 +93,7 @@ Code Structure
 Data
 ----
 
-In this example, we are using UCI (University of California, Irvine) [adult dataset](https://archive.ics.uci.edu/dataset/2/adult)
+In this example, we are using the UCI (University of California, Irvine) `adult dataset <https://archive.ics.uci.edu/dataset/2/adult>`_.
 
 The original dataset has already contains "training" and "test" datasets. Here we simply assume that "training" and test data sets are belong to different clients.
 so we assigned the training data and test data into two clients.
@@ -199,9 +201,11 @@ The results are stored in workspace "/tmp/nvflare"
     /tmp/nvflare/simulation/stats_df/server/simulate_job/statistics/adults_stats.json
 
 
-## Visualization
-   with json format, the data can be easily visualized via pandas dataframe and plots.
-   download and copy the output adults_stats.json file to demo directory, then you can run jupyter notebook visualization.ipynb
+Visualization
+-------------
+
+With JSON format, the data can be easily visualized via Pandas DataFrame and plots.
+Download and copy the output ``adults_stats.json`` file to the demo directory, then run the Jupyter notebook ``visualization.ipynb``.
 
 
 

diff --git a/docs/hello-world/hello-tf/index.rst b/docs/hello-world/hello-tf/index.rst
@@ -1,3 +1,5 @@
+.. _hello_tf:
+
 Hello TensorFlow
 ================
 
@@ -51,7 +53,7 @@ Code Structure
 
 .. code-block:: text
 
-   hello-pt
+   hello-tf
    |
    |-- client.py         # client local training script
    |-- model.py          # model definition

diff --git a/docs/how-to-guide/develop.rst b/docs/how-to-guide/develop.rst
@@ -0,0 +1,24 @@
+.. _how_to_develop_guide:
+
+####################
+How To Develop Guide
+####################
+
+How to install and turn existing stand-alone, centralized applications into federated learning applications.
+
+Installation Guide
+------------------
+.. toctree::
+   :maxdepth: 1
+
+   ../installation
+
+Federated Application
+---------------------
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   develop/*
+