Add client api DLI notebook

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_introduction.ipynb

@@ -0,0 +1,23 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Federated Statistics Introduction\n",
+    "\n",
+    "In a federated learning setting, because data is private at each site and we need to ensure data privacy, there are many considerations to take into account when trying to gather statistics on the data. We provide two examples, one for image data and one for tabular data:\n",
+    "\n",
+    "   * [Federated Statistics with image data](./federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb) shows how to compute local and global image statistics with the consideration that data is private at each of the client sites.\n",
+    "   * [Federated Statistics with tabular data](./federated_statistics_with_tabular_data/federated_statistics_with_tabular_data.ipynb) demonstrates how to create federated statistics for data that can be represented as Pandas DataFrames."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.2_convert_torch_lightning_to_federated_learning/convert_torch_lightning_to_fl.ipynb

@@ -424,7 +424,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -438,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/convert_ml_to_fl.ipynb

@@ -0,0 +1,34 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple ML/DL to FL transition with NVFlare\n",
+    "\n",
+    "Converting Deep Learning (DL) models to Federated Learning (FL) entails several key steps:\n",
+    "\n",
+    " - Formulating the algorithm: This involves determining how to adapt a DL model into an FL framework, including specifying the information exchange protocol between the server and clients.\n",
+    "\n",
+    " - Code conversion: Adapting existing standalone DL code into FL-compatible code. This typically involves minimal changes, often just a few lines of code, thanks to tools like NVFlare.\n",
+    "\n",
+    " - Workflow configuration: Once the code is modified, configuring the workflow to integrate the newly adapted FL code seamlessly.\n",
+    "\n",
+    "NVFlare simplifies the process of transitioning from traditional Machine Learning (ML) or DL algorithms to FL. With NVFlare, the conversion process requires only minor code adjustments.\n",
+    "\n",
+    "In this section, we have the following three examples for converting traditional ML to FL:\n",
+    "\n",
+    "   * [Convert Logistics Regression to federated learning](02.3.1_convert_logistic_regression_to_federated_learning/convert_logistic_regression_to_fl.ipynb)\n",
+    "   * [Convert KMeans to federated learning](02.3.2_convert_kmeans_to_federated_learning/convert_kmeans_to_fl.ipynb)\n",
+    "   * [Convert Survival Analysis to federated learning](02.3.3_convert_survival_analysis_to_federated_learning/convert_survival_analysis_to_fl.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}