restore

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/Chapter-1_running_federated_learning_applications/01.0_introduction/introduction.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Runing Federated Learning Applications\n",
+    "# Recap: Runing Federated Learning Applications\n",
     "\n",
     "\n",
     "In this chapter, we will explore the process of running federated learning applications. We will start by setting up the environment and preparing the data, followed by training a classifier using PyTorch. We will then convert deep learning models to federated learning, customize server and client logic, and setup track experiments. Finally, we will delve into the job structure and configurations, including running a simulator, and conclude with a recap of the covered topics.\n",

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/Chapter-1_running_federated_learning_applications/01.7_recap/recap.ipynb

@@ -5,43 +5,57 @@
    "id": "7b152728-3366-4432-adb1-29aa3051dc22",
    "metadata": {},
    "source": [
-    "# Recap\n",
+    "# Summary of Chapter 1\n",
     "\n",
-    "What we have learnt in Chapter-1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f2e3cb3-e61f-45e9-8dad-ad55ebb3641a",
-   "metadata": {},
-   "source": [
-    "## Chapter 1  Federated Learning Introduction\n",
+    "We cover a lot of materials in Chapter 1. We guide you through the process of running federated learning applications. Here is an overview of the key contents:\n",
     "\n",
-    "### Running Federated Learning Applications\n",
+    "1. **Running Federated Learning Job**\n",
+    "    - **Installation and Data Preparation**: Instructions for setting up the environment and preparing the data.\n",
+    "        - [setup.ipynb](../01.1_running_federated_learning_job/setup.ipynb)\n",
+    "    - **Training Classifier with PyTorch**: Steps to train a classifier using PyTorch in a federated learning setup.\n",
+    "        - [runing_pytorch_fl_job.ipynb](../01.1_running_federated_learning_job/runing_pytorch_fl_job.ipynb)\n",
     "\n",
-    "* Running federated hello-pytorch-lightning with simulator\n",
+    "2. **From Stand-Alone Deep Learning to Federated Learning**\n",
+    "    - **Conversion to Federated Learning**: Guide on converting deep learning models with PyTorch to federated learning.\n",
+    "        - [convert_dl_to_fl.ipynb](../01.2_convert_deep_learning_to_federated_learning/convert_dl_to_fl.ipynb)\n",
     "\n",
-    "* Understanding FL Job structure: client, server and Job\n",
+    "3. **Customizing the Federated Algorithms**\n",
+    "    - **Server Logic Customization**: Techniques to customize server logic for specific federated learning needs, we built an our own fed avg algorithms with best model seleciton, model saving and loading, as well as early stopping. \n",
+    "        - [customize_server_logics.ipynb](../01.3_customize_server_logics/customize_server_logics.ipynb)\n",
     "\n",
-    "* Understanding FL Job Concepts: controller, executor and configuration\n",
+    "4. **Adjusting Training Parameters**\n",
+    "    - **Client Logic Customization**: Methods to customize client logic to optimize training parameters. Here we show how to customize the training for each site. \n",
+    "        - [customize_client_training.ipynb](../01.4_customize_client_training/customize_client_training.ipynb)\n",
     "\n",
-    "* How to convert deep learning pytorch-lighting to federated learning\n",
+    "5. **Tracking Training Metrics**\n",
+    "    - **Experiment Tracking**: Tools and methods to track experiments and monitor training metrics effectively.\n",
+    "        - [experiment_tracking.ipynb](../01.5_experiment_tracking/experiment_tracking.ipynb)\n",
     "\n",
-    "* Customize server aggregation logic\n",
+    "6. **Job Structure and Configurations**\n",
+    "    - **Understanding Job Structure and Configuration**: Detailed explanation of the job structure and configurations necessary for running federated learning jobs.\n",
+    "        - [01.1.6.1_understanding_fl_job.ipynb](../01.6_job_structure_and_configuration/01.1.6.1_understanding_fl_job.ipynb)\n",
     "\n",
-    "* Custom client apps at different sites with different training parameters\n",
+    "7. **Recap of Covered Topics**\n",
+    "    - **Summary and Recap**: A recap of the topics covered in the previous sections.\n",
+    "        - [recap.ipynb](../01.7_recap/recap.ipynb)\n",
     "\n",
+    "Each section is designed to provide comprehensive guidance and practical examples to help you implement and customize federated learning in your applications. For detailed instructions and examples, refer to the respective notebooks linked in each section.\n",
     "\n",
-    "\n"
+    "\n",
+    "Now let's move on to the [Chapter 2](../../Chapter-2_develop_federated_learning_applications/02.0_introduction/introduction.ipynb\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53cbc090-833e-4be1-9f3e-a2e977026281",
+   "cell_type": "markdown",
+   "id": "4f2e3cb3-e61f-45e9-8dad-ad55ebb3641a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    " \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {