diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.0_introduction/introduction.ipynb b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.0_introduction/introduction.ipynb
index f4fb6dbb64..85352f1462 100644
--- a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.0_introduction/introduction.ipynb
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.0_introduction/introduction.ipynb
@@ -1,19 +1,47 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d86124d-3f0d-4cbd-8855-d1fe6877ee6c",
+   "cell_type": "markdown",
+   "id": "ed32af6f",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "# Introduction: Develop Federated Learning Applications\n",
+    "\n",
+    "In this chapter, we will explore the process of developing federated learning applications. We will start by exploring federated statistics and getting different visualizations from the data. We will then convert PyTorch Lightning code for use with NVFlare. We will then examine machine learning algorithms and look at how to convert logistics regression, kmeans, and survival analysis for use with federated learning. Finally, we will look into the Client API and conclude with a recap of the covered topics.\n",
+    "\n",
+    "\n",
+    "2.1. **Federated Statistics**\n",
+    "   * [Federated Statistics with image data](../02.1_federated_statistics/0federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb)\n",
+    "   * [Federated Statistics with tabular data](../02.1_federated_statistics/federated_statistics_with_tabular_data/federated_statistics_with_tabular_data.ipynb)\n",
+    "\n",
+    "2.2. **Convert PyTorch Lightning to Federated Learning**\n",
+    "\n",
+    "   * [Convert Torch Lightning to FL](../02.2_convert_torch_lightning_to_federated_learning/convert_torch_lightning_to_fl.ipynb)\n",
+    "\n",
+    "\n",
+    "2.3. **How to Convert Machine Learning Algorithms to Federated Algorithms**\n",
+    "\n",
+    "   * [Convert Logistics Regression to federated learning](../02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_Logistics_regression_to_federated_learning/convert_lr_to_fl.ipynb)\n",
+    "   * [Convert KMeans to federated learning](../02.3_convert_machine_learning_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_convert_machine_learning_to_federated_learning/02.3.3_convert_survival_analysis_to_federated_learning/convert_survival_analysis_to_fl.ipynb)\n",
+    "\n",
+    "2.4. **Client API** \n",
+    "\n",
+    "   * [NVFlare Client API](../02.4_client_api/Client_api.ipynb)\n",
+    "\n",
+    "2.5. [Recap of the covered topics](../02.5_recap/recap.ipynb)\n",
+    "\n",
+    "\n",
+    "\n",
+    "Let's get started with [Federated Statistics](../02.1_federated_statistics/0federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb)\n"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "nvflare_example",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "nvflare_example"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -25,7 +53,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/0federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/0federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb
deleted file mode 100644
index ecaa2524e5..0000000000
--- a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/0federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "46d9adef-b56f-4c12-ae96-7144c2845c1b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nvflare_example",
-   "language": "python",
-   "name": "nvflare_example"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/data/download_and_unzip_data.sh b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/data/download_and_unzip_data.sh
new file mode 100755
index 0000000000..a50bb1c504
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/data/download_and_unzip_data.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+DATASET_PATH="/tmp/nvflare/image_stats/data"
+
+if [ ! -d $DATASET_PATH ]; then
+  mkdir -p $DATASET_PATH
+fi
+
+source_url="$1"
+echo "download url = ${source_url}"
+if [ -n "${source_url}" ]; then
+  if [ ! -f "${DATASET_PATH}/COVID-19_Radiography_Dataset.zip" ]; then
+    wget -O "${DATASET_PATH}/COVID-19_Radiography_Dataset.zip" "${source_url}"
+  else
+    echo "zip file exists."
+  fi
+  if [ ! -d "${DATASET_PATH}/COVID-19_Radiography_Dataset" ]; then
+    unzip -d $DATASET_PATH "${DATASET_PATH}/COVID-19_Radiography_Dataset.zip"
+  else
+    echo "image files exist."
+  fi
+else
+  echo "empty URL, nothing downloaded, you need to provide real URL to download"
+fi
\ No newline at end of file
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/demo/image_statistics.json b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/demo/image_statistics.json
new file mode 100644
index 0000000000..45ba336e50
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/demo/image_statistics.json
@@ -0,0 +1 @@
+{"intensity": {"count": {"site-4": {"train": 1345}, "site-1": {"train": 3616}, "site-2": {"train": 6012}, "site-3": {"train": 10192}, "Global": {"train": 21165}}, "histogram": {"site-4": {"train": [[0.0, 1.0039, 7529944], [1.0039, 2.0078, 425108], [2.0078, 3.0118, 380736], [3.0118, 4.0157, 334702], [4.0157, 5.0196, 327862], [5.0196, 6.0235, 310277], [6.0235, 7.0275, 313290], [7.0275, 8.0314, 313654], [8.0314, 9.0353, 307805], [9.0353, 10.0392, 305356], [10.0392, 11.0431, 295868], [11.0431, 12.0471, 279067], [12.0471, 13.051, 270320], [13.051, 14.0549, 260414], [14.0549, 15.0588, 262861], [15.0588, 16.0627, 264740], [16.0627, 17.0667, 263759], [17.0667, 18.0706, 266840], [18.0706, 19.0745, 269593], [19.0745, 20.0784, 265030], [20.0784, 21.0824, 260416], [21.0824, 22.0863, 259147], [22.0863, 23.0902, 257797], [23.0902, 24.0941, 259564], [24.0941, 25.098, 259845], [25.098, 26.102, 256981], [26.102, 27.1059, 252589], [27.1059, 28.1098, 244041], [28.1098, 29.1137, 236778], [29.1137, 30.1176, 235530], [30.1176, 31.1216, 233887], [31.1216, 32.1255, 232690], [32.1255, 33.1294, 230496], [33.1294, 34.1333, 227944], [34.1333, 35.1373, 225518], [35.1373, 36.1412, 226460], [36.1412, 37.1451, 228242], [37.1451, 38.149, 230445], [38.149, 39.1529, 234045], [39.1529, 40.1569, 238784], [40.1569, 41.1608, 243710], [41.1608, 42.1647, 251529], [42.1647, 43.1686, 258775], [43.1686, 44.1726, 264193], [44.1726, 45.1765, 271677], [45.1765, 46.1804, 277858], [46.1804, 47.1843, 287293], [47.1843, 48.1882, 295818], [48.1882, 49.1922, 304070], [49.1922, 50.1961, 312974], [50.1961, 51.2, 323260], [51.2, 52.2039, 328831], [52.2039, 53.2078, 335326], [53.2078, 54.2118, 343748], [54.2118, 55.2157, 351160], [55.2157, 56.2196, 356396], [56.2196, 57.2235, 363184], [57.2235, 58.2275, 369794], [58.2275, 59.2314, 373595], [59.2314, 60.2353, 378877], [60.2353, 61.2392, 384616], [61.2392, 62.2431, 391297], [62.2431, 63.2471, 396375], [63.2471, 64.251, 398059], [64.251, 65.2549, 401636], [65.2549, 66.2588, 404846], [66.2588, 67.2627, 409274], [67.2627, 68.2667, 413504], [68.2667, 69.2706, 416709], [69.2706, 70.2745, 422904], [70.2745, 71.2784, 425087], [71.2784, 72.2824, 430026], [72.2824, 73.2863, 434464], [73.2863, 74.2902, 438169], [74.2902, 75.2941, 441835], [75.2941, 76.298, 447908], [76.298, 77.302, 454231], [77.302, 78.3059, 458792], [78.3059, 79.3098, 466910], [79.3098, 80.3137, 473704], [80.3137, 81.3176, 480384], [81.3176, 82.3216, 488587], [82.3216, 83.3255, 493441], [83.3255, 84.3294, 499632], [84.3294, 85.3333, 506173], [85.3333, 86.3373, 508150], [86.3373, 87.3412, 514601], [87.3412, 88.3451, 520894], [88.3451, 89.349, 523951], [89.349, 90.3529, 532177], [90.3529, 91.3569, 539180], [91.3569, 92.3608, 544994], [92.3608, 93.3647, 550783], [93.3647, 94.3686, 556833], [94.3686, 95.3726, 563344], [95.3726, 96.3765, 570413], [96.3765, 97.3804, 578149], [97.3804, 98.3843, 585887], [98.3843, 99.3882, 592452], [99.3882, 100.3922, 599573], [100.3922, 101.3961, 606518], [101.3961, 102.4, 612282], [102.4, 103.4039, 620579], [103.4039, 104.4078, 627741], [104.4078, 105.4118, 635267], [105.4118, 106.4157, 643762], [106.4157, 107.4196, 651374], [107.4196, 108.4235, 658375], [108.4235, 109.4275, 664897], [109.4275, 110.4314, 672678], [110.4314, 111.4353, 680048], [111.4353, 112.4392, 688321], [112.4392, 113.4431, 694140], [113.4431, 114.4471, 702296], [114.4471, 115.451, 710582], [115.451, 116.4549, 715170], [116.4549, 117.4588, 724991], [117.4588, 118.4627, 733420], [118.4627, 119.4667, 741582], [119.4667, 120.4706, 747743], [120.4706, 121.4745, 752486], [121.4745, 122.4784, 759045], [122.4784, 123.4824, 767290], [123.4824, 124.4863, 773984], [124.4863, 125.4902, 779475], [125.4902, 126.4941, 786162], [126.4941, 127.498, 791886], [127.498, 128.502, 798187], [128.502, 129.5059, 807763], [129.5059, 130.5098, 812860], [130.5098, 131.5137, 820695], [131.5137, 132.5177, 830039], [132.5177, 133.5216, 837940], [133.5216, 134.5255, 844831], [134.5255, 135.5294, 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diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/demo/visualization.ipynb b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/demo/visualization.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3f851980",
+   "metadata": {},
+   "source": [
+    "# NVFLARE Federated Statistics Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aea2083",
+   "metadata": {},
+   "source": [
+    "#### Dependencies\n",
+    "\n",
+    "To run this example, you need to install the following dependencies:\n",
+    "* monai[itk]\n",
+    "* numpy\n",
+    "* pandas\n",
+    "* kaleido\n",
+    "* matplotlib\n",
+    "* jupyter\n",
+    "* notebook\n",
+    "\n",
+    "These are captured in the requirements.txt\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b71dd55",
+   "metadata": {},
+   "source": [
+    "## Image Statistics Visualization\n",
+    "In this example, we demonstate how to visualize the results from the statistics of image data. The visualization requires json, pandas, matplotlib modules as well as nvflare visualization utlities. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85f23acf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "from nvflare.app_opt.statistics.visualization.statistics_visualization import Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "151e23a8",
+   "metadata": {},
+   "source": [
+    "First, copy the resulting json file to demo directory. In this example, resulting file is called image_statistics.json. Then load json file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "44f6bed2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with open('image_statistics.json', 'r') as f:\n",
+    "    data = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4b83ddb",
+   "metadata": {},
+   "source": [
+    "Initialize the Visualization utilities\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ab771712",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vis = Visualization()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49f976aa",
+   "metadata": {},
+   "source": [
+    "### Overall Statistics\n",
+    "vis.show_stats() will show the statistics for each features, at each site for each dataset\n",
+    "\n",
+    "vis.show_stats(data = data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "20ea4dff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "intensity\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>histogram</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>site-4-train</th>\n",
+       "      <td>1345</td>\n",
+       "      <td>[[0.0, 1.0039, 7529944], [1.0039, 2.0078, 4251...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>site-1-train</th>\n",
+       "      <td>3616</td>\n",
+       "      <td>[[0.0, 1.0039, 10894028], [1.0039, 2.0078, 919...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>site-2-train</th>\n",
+       "      <td>6012</td>\n",
+       "      <td>[[0.0, 1.0039, 15941521], [1.0039, 2.0078, 340...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>site-3-train</th>\n",
+       "      <td>10192</td>\n",
+       "      <td>[[0.0, 1.0039, 35420536], [1.0039, 2.0078, 343...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Global-train</th>\n",
+       "      <td>21165</td>\n",
+       "      <td>[[0.0, 1.0039, 69786029], [1.0039, 2.0078, 818...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              count                                          histogram\n",
+       "site-4-train   1345  [[0.0, 1.0039, 7529944], [1.0039, 2.0078, 4251...\n",
+       "site-1-train   3616  [[0.0, 1.0039, 10894028], [1.0039, 2.0078, 919...\n",
+       "site-2-train   6012  [[0.0, 1.0039, 15941521], [1.0039, 2.0078, 340...\n",
+       "site-3-train  10192  [[0.0, 1.0039, 35420536], [1.0039, 2.0078, 343...\n",
+       "Global-train  21165  [[0.0, 1.0039, 69786029], [1.0039, 2.0078, 818..."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis.show_stats(data = data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "521cbf6f",
+   "metadata": {},
+   "source": [
+    "### select features statistics using white_list_features \n",
+    "user can optionally select only show specified features via white_list_features arguments. In these image files, we only have one feature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ab23bcc",
+   "metadata": {},
+   "source": [
+    "### Histogram Visualization\n",
+    "We can use vis.show_histograms() to visualize the histogram. Before we do that, we need set some iPython display setting to make sure the graph displayed in full cell. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4bada64b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:100%  depth:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100%  depth:100% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e415b49e",
+   "metadata": {},
+   "source": [
+    "The following command display histograms for numberic features. The result shows both main plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "53542cf9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis.show_histograms(data = data, plot_type=\"main\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8d537cc",
+   "metadata": {},
+   "source": [
+    "## Display Options\n",
+    "Similar to other statistics, we can use white_list_features to select only few features to display histograms. We can also use display_format=\"percent\" to allow all dataset and sites to be displayed in the same scale. User can set \n",
+    "\n",
+    "* display_format: \"percent\" or \"sample_count\"\n",
+    "* white_list_features: feature names\n",
+    "* plot_type : \"both\" or \"main\" or \"subplot\"\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "353db4d9",
+   "metadata": {},
+   "source": [
+    "#### show default display format with subplot\n",
+    "In the following, we display only feature \"Intensity\" in default display_format, with \"subplot\" plot_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f619729",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis.show_histograms(data = data, plot_type=\"subplot\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbf7fc73",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### show percent display format with default plot_type (main)\n",
+    "In the following, we display only feature \"Intensity\" in \"percent\" display_format, with default plot_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8655ad63",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis.show_histograms(data = data, display_format=\"percent\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe527f64",
+   "metadata": {},
+   "source": [
+    "### Tip: Avoid repeated calculation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4640b9aa",
+   "metadata": {},
+   "source": [
+    "If you intend to plot histogram main plot and subplot separately, repeated calling show_histogram with different plot_types is not efficicent, as it repeatewd calculate the same set of Dataframes. To do it efficiently, you can use the following functions instead show_histogram methods. This avoid the duplicated calculation in show_histograms. But if you intend to show both plots, the show_histogram() should be used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8e6722f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feature_dfs = vis.get_histogram_dataframes(data, display_format=\"percent\" )\n",
+    "   \n",
+    "vis.show_dataframe_plots(feature_dfs, plot_type=\"main\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "17fbd7fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis.show_dataframe_plots(feature_dfs, plot_type=\"subplot\")   "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venvpt",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/figs/image_histogram.png b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/figs/image_histogram.png
new file mode 100644
index 0000000000..94503bd053
Binary files /dev/null and b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/figs/image_histogram.png differ
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/utils/prepare_data.py b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/utils/prepare_data.py
new file mode 100644
index 0000000000..fd8465649a
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats/utils/prepare_data.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import glob
+import json
+import os
+import random
+
+SEED = 0
+
+
+def create_datasets(root, subdirs, extension, shuffle, seed):
+    random.seed(seed)
+
+    data_lists = []
+    for subdir in subdirs:
+        search_string = os.path.join(root, "**", subdir, "images", "*" + extension)
+        data_list = glob.glob(search_string, recursive=True)
+
+        assert (
+            len(data_list) > 0
+        ), f"No images found using {search_string} for subdir '{subdir}' and extension '{extension}'!"
+
+        if shuffle:
+            random.shuffle(data_list)
+
+        data_lists.append(data_list)
+
+    return data_lists
+
+
+def save_data_list(data, data_list_file, data_root, key="data"):
+    data_list = []
+    for d in data:
+        data_list.append({"image": d.replace(data_root + os.path.sep, "")})
+
+    os.makedirs(os.path.dirname(data_list_file), exist_ok=True)
+    with open(data_list_file, "w") as f:
+        json.dump({key: data_list}, f, indent=4)
+
+    print(f"Saved {len(data_list)} entries at {data_list_file}")
+
+
+def prepare_data(
+    input_dir: str,
+    input_ext: str = ".png",
+    output_dir: str = "/tmp/nvflare/image_stats/data",
+    sub_dirs: str = "COVID,Lung_Opacity,Normal,Viral Pneumonia",
+):
+    sub_dir_list = [sd for sd in sub_dirs.split(",")]
+
+    data_lists = create_datasets(root=input_dir, subdirs=sub_dir_list, extension=input_ext, shuffle=True, seed=SEED)
+    print(f"Created {len(data_lists)} data lists for {sub_dir_list}.")
+
+    site_id = 1
+    for subdir, data_list in zip(sub_dir_list, data_lists):
+        save_data_list(data_list, os.path.join(output_dir, f"site-{site_id}_{subdir}.json"), data_root=input_dir)
+        site_id += 1
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", type=str, required=True, help="Location of image files")
+    parser.add_argument("--input_ext", type=str, default=".png", help="Search extension")
+    parser.add_argument(
+        "--output_dir", type=str, default="/tmp/nvflare/image_stats/data", help="Output location of data lists"
+    )
+    parser.add_argument(
+        "--subdirs",
+        type=str,
+        default="COVID,Lung_Opacity,Normal,Viral Pneumonia",
+        help="A list of sub-folders to include.",
+    )
+    args = parser.parse_args()
+
+    assert "," in args.subdirs, "Expecting a comma separated list of subdirs names"
+
+    prepare_data(args.input_dir, args.input_ext, args.output_dir, args.subdirs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats_job.py b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats_job.py
new file mode 100644
index 0000000000..0dcbcd735c
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/image_stats_job.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+
+from src.image_statistics import ImageStatistics
+
+from nvflare.job_config.stats_job import StatsJob
+
+
+def define_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-n", "--n_clients", type=int, default=3)
+    parser.add_argument("-d", "--data_root_dir", type=str, nargs="?", default="/tmp/nvflare/image_stats/data")
+    parser.add_argument("-o", "--stats_output_path", type=str, nargs="?", default="statistics/stats.json")
+    parser.add_argument("-j", "--job_dir", type=str, nargs="?", default="/tmp/nvflare/jobs/image_stats")
+    parser.add_argument("-w", "--work_dir", type=str, nargs="?", default="/tmp/nvflare/workspace/image_stats")
+    parser.add_argument("-co", "--export_config", action="store_true", help="config only mode, export config")
+
+    return parser.parse_args()
+
+
+def main():
+    args = define_parser()
+
+    n_clients = args.n_clients
+    data_root_dir = args.data_root_dir
+    output_path = args.stats_output_path
+    job_dir = args.job_dir
+    work_dir = args.work_dir
+    export_config = args.export_config
+
+    statistic_configs = {"count": {}, "histogram": {"*": {"bins": 20, "range": [0, 256]}}}
+    # define local stats generator
+    stats_generator = ImageStatistics(data_root_dir)
+
+    job = StatsJob(
+        job_name="stats_image",
+        statistic_configs=statistic_configs,
+        stats_generator=stats_generator,
+        output_path=output_path,
+    )
+
+    sites = [f"site-{i + 1}" for i in range(n_clients)]
+    job.setup_clients(sites)
+
+    if export_config:
+        job.export_job(job_dir)
+    else:
+        job.simulator_run(work_dir, gpu="0")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/requirements.txt b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/requirements.txt
new file mode 100644
index 0000000000..6937813465
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/requirements.txt
@@ -0,0 +1,8 @@
+numpy
+monai[itk]
+pandas
+kaleido
+matplotlib
+jupyter
+notebook
+tdigest
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/src/image_statistics.py b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/src/image_statistics.py
new file mode 100644
index 0000000000..3bfe2ea61c
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/code/src/image_statistics.py
@@ -0,0 +1,133 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import glob
+import os
+from typing import Dict, List, Optional
+
+import numpy as np
+from monai.data import ITKReader, load_decathlon_datalist
+from monai.transforms import LoadImage
+
+from nvflare.apis.fl_context import FLContext
+from nvflare.app_common.abstract.statistics_spec import Bin, DataType, Feature, Histogram, HistogramType, Statistics
+from nvflare.security.logging import secure_log_traceback
+
+
+class ImageStatistics(Statistics):
+    def __init__(self, data_root: str = "/tmp/nvflare/image_stats/data", data_list_key: str = "data"):
+        """local image statistics generator .
+
+        Args:
+            data_root: directory with local image data.
+            data_list_key: data list key to use.
+        Returns:
+            a Shareable with the computed local statistics`
+        """
+        super().__init__()
+        self.data_list_key = data_list_key
+        self.data_root = data_root
+        self.data_list = None
+        self.client_name = None
+
+        self.loader = None
+        self.failure_images = 0
+        self.fl_ctx = None
+
+    def initialize(self, fl_ctx: FLContext):
+        self.fl_ctx = fl_ctx
+        self.client_name = fl_ctx.get_identity_name()
+        self.loader = LoadImage(image_only=True)
+        self.loader.register(ITKReader())
+        self._load_data_list(self.client_name, fl_ctx)
+
+        if self.data_list is None:
+            raise ValueError("data is not loaded. make sure the data is loaded")
+
+    def _load_data_list(self, client_name, fl_ctx: FLContext) -> bool:
+        dataset_json = glob.glob(os.path.join(self.data_root, client_name + "*.json"))
+        if len(dataset_json) != 1:
+            self.log_error(
+                fl_ctx, f"No unique matching dataset list found in {self.data_root} for client {client_name}"
+            )
+            return False
+        dataset_json = dataset_json[0]
+        self.log_info(fl_ctx, f"Reading data from {dataset_json}")
+
+        data_list = load_decathlon_datalist(
+            data_list_file_path=dataset_json, data_list_key=self.data_list_key, base_dir=self.data_root
+        )
+        self.data_list = {"train": data_list}
+
+        self.log_info(fl_ctx, f"Client {client_name} has {len(self.data_list)} images")
+        return True
+
+    def pre_run(
+        self,
+        statistics: List[str],
+        num_of_bins: Optional[Dict[str, Optional[int]]],
+        bin_ranges: Optional[Dict[str, Optional[List[float]]]],
+    ):
+        return {}
+
+    def features(self) -> Dict[str, List[Feature]]:
+        return {"train": [Feature("intensity", DataType.FLOAT)]}
+
+    def count(self, dataset_name: str, feature_name: str) -> int:
+        image_paths = self.data_list[dataset_name]
+        return len(image_paths)
+
+    def failure_count(self, dataset_name: str, feature_name: str) -> int:
+
+        return self.failure_images
+
+    def histogram(
+        self, dataset_name: str, feature_name: str, num_of_bins: int, global_min_value: float, global_max_value: float
+    ) -> Histogram:
+        histogram_bins: List[Bin] = []
+        histogram = np.zeros((num_of_bins,), dtype=np.int64)
+        bin_edges = []
+        for i, entry in enumerate(self.data_list[dataset_name]):
+            file = entry.get("image")
+            try:
+                img = self.loader(file)
+                curr_histogram, bin_edges = np.histogram(
+                    img, bins=num_of_bins, range=(global_min_value, global_max_value)
+                )
+                histogram += curr_histogram
+                bin_edges = bin_edges.tolist()
+
+                if i % 100 == 0:
+                    self.logger.info(
+                        f"{self.client_name}, adding {i + 1} of {len(self.data_list[dataset_name])}: {file}"
+                    )
+            except Exception as e:
+                self.failure_images += 1
+                self.logger.critical(
+                    f"Failed to load file {file} with exception: {e.__str__()}. " f"Skipping this image..."
+                )
+
+        if num_of_bins + 1 != len(bin_edges):
+            secure_log_traceback()
+            raise ValueError(
+                f"bin_edges size: {len(bin_edges)} is not matching with number of bins + 1: {num_of_bins + 1}"
+            )
+
+        for j in range(num_of_bins):
+            low_value = bin_edges[j]
+            high_value = bin_edges[j + 1]
+            bin_sample_count = histogram[j]
+            histogram_bins.append(Bin(low_value=low_value, high_value=high_value, sample_count=bin_sample_count))
+
+        return Histogram(HistogramType.STANDARD, histogram_bins)
diff --git a/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb
new file mode 100644
index 0000000000..54831a552f
--- /dev/null
+++ b/examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.1_federated_statistics/federated_statistics_with_image_data/federated_statistics_with_image_data.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "26cb3afa",
+   "metadata": {},
+   "source": [
+    "# Federated Statistics with image data\n",
+    "\n",
+    "## Calculate Image Histogram\n",
+    "\n",
+    "In this example, we will compute local and global image statistics with the consideration that data is private at each of the client sites."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64a17f22-5667-4f99-b4f6-d49116db74b0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Install requirements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c8969bf-d010-42b5-a807-0808922402d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%pip install -r code/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0065b351-baac-4f84-aa15-3d875f86cb93",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Download data\n",
+    "\n",
+    "As an example, we use the dataset from the [\"COVID-19 Radiography Database\"](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database).\n",
+    "it contains png image files in four different classes: `COVID`, `Lung_Opacity`, `Normal`, and `Viral Pneumonia`.\n",
+    "First create a temp directory, then we download and extract to `/tmp/nvflare/image_stats/data/.`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b4e64769-17f1-4805-9399-1c141e050065",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%bash \n",
+    "\n",
+    "# prepare the directory\n",
+    "\n",
+    "if [ ! -d /tmp/nvflare/image_stats/data ]; then\n",
+    "  mkdir -p /tmp/nvflare/image_stats/data\n",
+    "fi\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0562f713-5892-43c7-a3d6-d277c337b5ea",
+   "metadata": {},
+   "source": [
+    "Download and unzip the data (you may need to log in to Kaggle or use an API key). Once you have extracted the data from the zip file, you can check the directory to make sure you have the COVID-19_Radiography_Dataset directory at the following location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdc68ebf-6071-479d-8cc1-15439bedea02",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ls -l /tmp/nvflare/image_stats/data/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94faaa6b-08fd-485c-87d5-53b4520177fe",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "## Prepare data\n",
+    "\n",
+    "Next, create the data lists simulating different clients with varying amounts and types of images. \n",
+    "The downloaded archive contains subfolders for four different classes: `COVID`, `Lung_Opacity`, `Normal`, and `Viral Pneumonia`.\n",
+    "Here we assume each class of image corresponds to a different site."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1ea959f-7282-4e55-bb26-11524ec47e99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from code.image_stats.utils.prepare_data import prepare_data\n",
+    "\n",
+    "prepare_data(input_dir = \"/tmp/nvflare/image_stats/data\", \n",
+    "             input_ext = \".png\",\n",
+    "             output_dir =\"/tmp/nvflare/image_stats/data\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00de5e4-4360-4fc5-a819-4eb156e56341",
+   "metadata": {},
+   "source": [
+    "## Run Job with FL Simulator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e972070",
+   "metadata": {},
+   "source": [
+    "The file [image_stats_job.py](code/image_stats_job.py) uses the StatsJob to generate a job configuration in a Pythonic way. With the default arguments, the job will be exported to `/tmp/nvflare/jobs/image_stats` and then the job will be run with the FL simulator with the `simulator_run()` command with a work_dir of `/tmp/nvflare/workspace/image_stats`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0db7cd1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python3 code/image_stats_job.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a09aed14-5011-4418-8840-5f7c16c97534",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Examine the result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45bf6e9a-3265-4e45-8b06-c8e543605f21",
+   "metadata": {},
+   "source": [
+    "\n",
+    "The results are stored on the server in the workspace at \"/tmp/nvflare/image_stats\" and can be accessed with the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "112a7dd0-45d9-42ea-98b2-f72a3bbccf48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! ls -al /tmp/nvflare/workspace/image_stats/server/simulate_job/statistics/image_statistics.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cd042db-6ce0-4e37-bcbe-d96051e4d164",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Visualization\n",
+    "We can visualize the results easly via the visualization notebook. Before we do that, we need to copy the data to the notebook directory \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a3c89693-37b9-450c-85dd-8a2d78fee3fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! cp /tmp/nvflare/workspace/image_stats/server/simulate_job/statistics/image_statistics.json image_stats/demo/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c6f632-3326-4236-902e-8c0965688d85",
+   "metadata": {},
+   "source": [
+    "now we can visualize via the [visualization notebook](image_stats/demo/visualization.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fda06c0b-798d-480d-9b4c-a62fab95bcf0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## We are done !\n",
+    "Congratulations, you just completed the federated stats image histogram calulation\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venvpt",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}