The Interface.ipynb

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      "source": [
        "### Installing dependencies\n",
        "\n",
        "Next, we install the necessary packages for PyTorch (`torch` and `torchvision`) and Flower (`flwr`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "8dW1w5RgaD7H",
        "outputId": "cbcf4c07-ae9d-4bf7-9f1b-fde50f38f7aa"
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (4.11.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4) (2.4.1)\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import ipywidgets as widgets\n",
        "from IPython.display import display, clear_output\n",
        "!pip install beautifulsoup4\n",
        "from bs4 import BeautifulSoup\n",
        "from IPython.display import display, HTML, clear_output\n",
        "from ipywidgets import Button, Layout\n",
        "import threading\n",
        "import ipywidgets as widgets\n",
        "import IPython.display as display_output\n",
        "from google.colab import drive \n",
        "drive.mount('/content/drive', force_remount=True) \n",
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import random_split, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install -q flwr[simulation] torch torchvision matplotlib\n",
        "\n",
        "from collections import OrderedDict\n",
        "from typing import List, Tuple\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        " \n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        " \n",
        "import flwr as fl\n",
        "from flwr.common import Metrics\n",
        "DEVICE = torch.device(\"cpu\")\n",
        "    # Mount Google Drive\n",
        "\n",
        "# Specify client resources if you need GPU (defaults to 1 CPU and 0 GPU)\n",
        "client_resources = None\n",
        "if DEVICE.type == \"cuda\":\n",
        "    client_resources = {\"num_gpus\": 1}\n",
        "# Set device\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<h1 style=\"color:red; width:100px;\" >____________WELLCOME____________</h1>"
            ]
          },
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        {
          "output_type": "display_data",
          "data": {
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            "text/html": [
              "<h1 style=\"color:blue; width:100px;\" >THE_FEDERATED_LEARNING_IN_SMART</h1>"
            ]
          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<h1 style=\"color:blue; width:100px;\">____________HEALTHCARE___________</h1>"
            ]
          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label(value='NUMBER_of_hospital:')"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "dda1a2194f384881a665a290c5ba9f9f"
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              "Dropdown(options=(1, 5, 10, 100), value=1)"
            ],
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              "version_major": 2,
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              "model_id": "0ce5e0ffaca346e08c0f792a1ff49b0c"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label(value='Dataset Choice:')"
            ],
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              "version_minor": 0,
              "model_id": "8930000d933b47d4b8044e4f59789daa"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dropdown(options=('COVID_19', 'PNEUMONIA', 'Alzheimer_s', 'brian_tumor'), value='COVID_19')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Button(description='Load Datasets', style=ButtonStyle())"
            ],
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              "model_id": "cf9a64c332904b33a91929636c7e9f70"
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            "text/html": [
              "<h3 style=\"color:red; width:100px;\">NEXT STEP</h3>"
            ]
          },
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            "text/html": [
              "<h1 style=\"color:blue; width:100px;\">Centralized_Training</h1>"
            ]
          },
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        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label(value='Train Central')"
            ],
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            "text/plain": [
              "Button(description='Yes', style=ButtonStyle())"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "IntText(value=0, description='NUM_EPOCHS', layout=Layout(display='none'))"
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        {
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              "Button(button_style='success', description='Train', layout=Layout(display='none'), style=ButtonStyle())"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
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              "version_minor": 0,
              "model_id": "c21f965942d549e69ccd43183ac91912"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<h3 style=\"color:red; width:100px;\">NEXT STEP</h3>"
            ]
          },
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          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<h1 style=\"color:blue; width:100px;\">FEDRATED_LEARNIN_WITH_FLOWER</h1>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Dropdown(description='N_Rounds:', options=(1, 2, 5, 10, 100), value=1), Dropdown(description='S…"
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              "<h3 style=\"color:red; width:100px;\">THE_END</h3>"
            ]
          },
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            "text/html": [
              "<h1 style=\"color:blue; width:100px;\">THE_OUTPUT_CLASSES_OF_DATASET</h1>"
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      "source": [
        "# Set the layout for the button\n",
        "button_layout = widgets.Layout(width='auto')\n",
        "\n",
        "# Create a label for printing the size of the data\n",
        "size_label = widgets.Label()\n",
        "\n",
        "mess='<h1 style=\"color:red; width:100px;\" >____________WELLCOME____________</h1>'\n",
        "display(HTML(mess))\n",
        "cen='<h1 style=\"color:blue; width:100px;\" >THE_FEDERATED_LEARNING_IN_SMART</h1>'\n",
        "cenn='<h1 style=\"color:blue; width:100px;\">____________HEALTHCARE___________</h1>'\n",
        "display(HTML(cen))  \n",
        "display(HTML(cenn)) \n",
        "# Create a label for the description of the dataset choice\n",
        "description = widgets.Label(value='NUMBER_of_hospital:')\n",
        "description_label = widgets.Label(value='Dataset Choice:')\n",
        "\n",
        "# Create the widgets\n",
        "num_clients_input = widgets.Dropdown(description='', options=[1, 5, 10, 100])\n",
        "dataset_choice_dropdown = widgets.Dropdown(description='', options=['COVID_19', 'PNEUMONIA', 'Alzheimer_s', 'brian_tumor'])\n",
        "button = widgets.Button(description='Load Datasets')\n",
        "button = widgets.Button(description='Load Datasets')\n",
        "# Function to load datasets and assign values to the global variables\n",
        "def load_datasets(num_clients, dataset_choice):\n",
        "    global num_c, trainloaders, valloaders, testloader, model, classes\n",
        "\n",
        "    # Dataset paths\n",
        "    dataset_paths = {\n",
        "        'COVID_19': '/content/drive/MyDrive/data/dataset/COVID_19',\n",
        "        'PNEUMONIA': '/content/drive/MyDrive/data/dataset/PNEUMONIA',\n",
        "        'Alzheimer_s': '/content/drive/MyDrive/data/dataset/Alzheimer_s Dataset',\n",
        "        'brian_tumor': '/content/drive/MyDrive/data/dataset/brian_tumor_dataset'\n",
        "    }\n",
        "\n",
        "    datatrain = f'{dataset_paths[dataset_choice]}/train'\n",
        "    datatset = f'{dataset_paths[dataset_choice]}/test'\n",
        "    BATCH_SIZE = 32\n",
        "    img_size = (224, 224)\n",
        "\n",
        "    # Download and transform MRI Brain Tumor Detector dataset\n",
        "    transform = transforms.Compose([\n",
        "        transforms.Grayscale(),\n",
        "        transforms.Resize(img_size),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5,), (0.5,))\n",
        "    ])\n",
        "\n",
        "    trainset = ImageFolder(datatrain, transform=transform)\n",
        "    testset = ImageFolder(datatset, transform=transform)\n",
        "    size_label.value = f'The size of data: {len(trainset) + len(testset)}'\n",
        "\n",
        "    # Get the classes from the trainset\n",
        "    classes = trainset.classes\n",
        "    num_c = len(classes)\n",
        "\n",
        "    # Split training set into num_clients partitions to simulate the individual dataset\n",
        "    partition_size = len(trainset) // num_clients\n",
        "    lengths = [partition_size] * num_clients\n",
        "    datasets = random_split(trainset, lengths, torch.Generator().manual_seed(42))\n",
        "\n",
        "    # Split each partition into train/val and create DataLoader\n",
        "    trainloaders = []\n",
        "    valloaders = []\n",
        "    for ds in datasets:\n",
        "        len_val = len(ds) // 10  # 10 % validation set\n",
        "        len_train = len(ds) - len_val\n",
        "        lengths = [len_train, len_val]\n",
        "        ds_train, ds_val = random_split(ds, lengths, torch.Generator().manual_seed(42))\n",
        "        trainloaders.append(DataLoader(ds_train, batch_size=BATCH_SIZE, shuffle=True))\n",
        "        valloaders.append(DataLoader(ds_val, batch_size=BATCH_SIZE))\n",
        "    testloader = DataLoader(testset, batch_size=BATCH_SIZE)\n",
        "    return trainloaders, valloaders, testloader, classes,num_c\n",
        "\n",
        "# Create the neural network model\n",
        "class Net(nn.Module):\n",
        "    def __init__(self, num_c):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 32, 3, padding=1)\n",
        "        self.pool1 = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "        self.pool2 = nn.MaxPool2d(2, 2)\n",
        "        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)\n",
        "        self.pool3 = nn.MaxPool2d(2, 2)\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.fc1 = nn.Linear(128 * 28 * 28, 264)\n",
        "        self.fc2 = nn.Linear(264,num_c)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = nn.ReLU()(x)\n",
        "        x = self.pool1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = nn.ReLU()(x)\n",
        "        x = self.pool2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = nn.ReLU()(x)\n",
        "        x = self.pool3(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.flatten(x)\n",
        "        x = self.fc1(x)\n",
        "        x = nn.ReLU()(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# Function to train the network\n",
        "def train(net, trainloader, epochs: int, verbose=False):\n",
        "    \"\"\"Train the network on the training set.\"\"\"\n",
        "    criterion = torch.nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.Adam(net.parameters())\n",
        "    net.train()\n",
        "    for epoch in range(epochs):\n",
        "        correct, total, epoch_loss = 0, 0, 0.0\n",
        "        for images, labels in trainloader:\n",
        "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = net(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            # Metrics\n",
        "            epoch_loss += loss\n",
        "            total += labels.size(0)\n",
        "            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()\n",
        "        epoch_loss /= len(trainloader.dataset)\n",
        "        epoch_acc = correct / total\n",
        "        if verbose:\n",
        "            print(f\"Epoch {epoch+1}: train loss {epoch_loss}, accuracy {epoch_acc}\")\n",
        "\n",
        "# Function to test the network\n",
        "def test(net, testloader):\n",
        "    \"\"\"Evaluate the network on the entire test set.\"\"\"\n",
        "    criterion = torch.nn.CrossEntropyLoss()\n",
        "    correct, total, loss = 0, 0, 0.0\n",
        "    net.eval()\n",
        "    with torch.no_grad():\n",
        "        for images, labels in testloader:\n",
        "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
        "            outputs = net(images)\n",
        "            loss += criterion(outputs, labels).item()\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "    loss /= len(testloader.dataset)\n",
        "    accuracy = correct / total\n",
        "    return loss, accuracy\n",
        "\n",
        "# Register the button callback\n",
        "def button_clicked(b):\n",
        "    global classes, trainloaders, valloaders, testloader, model, num_clients, num_c  # Use the global variables\n",
        "\n",
        "    num_clients = num_clients_input.value\n",
        "    dataset_choice = dataset_choice_dropdown.value\n",
        "    load_datasets(num_clients, dataset_choice)\n",
        "\n",
        "    # Create the neural network model\n",
        "    model = Net(num_c)\n",
        "    print(\"Number of classes:\", num_c)\n",
        "    print('num_clients',num_clients)\n",
        "    print('Waiting for the plot to display...')\n",
        "\n",
        "    # Create a dictionary to store one image from each class\n",
        "    class_images = {}\n",
        "\n",
        "    # Iterate over the trainloaders to get one image from each class\n",
        "    for trainloader in trainloaders:\n",
        "        for images, labels in trainloader:\n",
        "            for image, label in zip(images, labels):\n",
        "                class_name = classes[label]\n",
        "                if class_name not in class_images:\n",
        "                    # Reshape and convert the image to a NumPy array\n",
        "                    image = image.permute(1, 2, 0).numpy()\n",
        "                    # Denormalize\n",
        "                    image = image / 2 + 0.5\n",
        "                    class_images[class_name] = image\n",
        "                    break\n",
        "\n",
        "    # Create a figure and a grid of subplots\n",
        "    fig, axs = plt.subplots(1, len(class_images), figsize=(5, 3))\n",
        "\n",
        "    # Loop over the classes and plot the corresponding image\n",
        "    for i, (class_name, image) in enumerate(class_images.items()):\n",
        "        ax = axs[i]\n",
        "        ax.imshow(image)\n",
        "        ax.set_title(class_name)\n",
        "        ax.axis(\"off\")\n",
        "\n",
        "    # Set the title of the plot\n",
        "    fig.suptitle('Sample of Selected MRI Datasets', fontsize=14)\n",
        "\n",
        "    # Show the plot\n",
        "    plt.tight_layout()\n",
        "    plt.rcParams['figure.dpi'] = 100\n",
        "\n",
        "    # Create an output widget\n",
        "    output_widget = widgets.Output()\n",
        "\n",
        "    # Display the plot inside the output widget\n",
        "    with output_widget:\n",
        "        plt.show()\n",
        "\n",
        "    # Clear the previous output, if any\n",
        "    #display_output.clear_output(wait=True)\n",
        "\n",
        "    # Display the output widget\n",
        "    display_output.display(output_widget)\n",
        "    \n",
        "    # Create a label widget for the output\n",
        "    \n",
        "#///////////////////////\n",
        "    \n",
        "\n",
        "# Register the button callback\n",
        "button.on_click(button_clicked)\n",
        "\n",
        "# Display the widgets\n",
        "\n",
        "display(description)\n",
        "display(num_clients_input)\n",
        "display(description_label)\n",
        "display(dataset_choice_dropdown)\n",
        "display(size_label)\n",
        "display(button, layout=button_layout)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "message = '<h3 style=\"color:red; width:100px;\">NEXT STEP</h3>'\n",
        "display(HTML(message))\n",
        "cen = '<h1 style=\"color:blue; width:100px;\">Centralized_Training</h1>'\n",
        "display(HTML(cen))  \n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display, clear_output\n",
        "\n",
        "# Create widgets\n",
        "label_train_central = widgets.Label(value='Train Central')\n",
        "button_yes = widgets.Button(description='Yes')\n",
        "button_no = widgets.Button(description='No')\n",
        "input_num_epochs = widgets.IntText(description='NUM_EPOCHS')\n",
        "button_train = widgets.Button(description='Train', button_style='success')\n",
        "output_train_epoch = widgets.Output()\n",
        "\n",
        "# Define button click handlers\n",
        "def on_button_yes_clicked(button):\n",
        "    button_yes.layout.display = 'none'\n",
        "    button_no.layout.display = 'none'\n",
        "    input_num_epochs.layout.display = ''\n",
        "    button_train.layout.display = 'flex'\n",
        "\n",
        "def on_button_no_clicked(button):\n",
        "    button_yes.layout.display = 'none'\n",
        "    button_no.layout.display = 'none'\n",
        "    button_train.layout.display = 'none'\n",
        "    print('OK')\n",
        "\n",
        "def on_button_train_clicked(button):\n",
        "        trainloader = trainloaders[input_num_epochs.value]\n",
        "        input_num_epochs.layout.display = 'none'\n",
        "        button_train.layout.display = 'none'\n",
        "        with output_train_epoch:\n",
        "            clear_output()  # Clear previous output\n",
        "        accuracy_history = []   \n",
        "        for epoch in range(input_num_epochs.value):\n",
        "            train(model, trainloader, 1)\n",
        "            loss, accuracy = test(model, testloader)\n",
        "            accuracy_history.append(accuracy)  # Append accuracy to history\n",
        "            print(f\"Epoch {epoch+1}: test loss {loss}, accuracy {accuracy}\")\n",
        "        message = '<h4 style=\"color:red; width:100px;\">training is over</h4>'\n",
        "        display(HTML(message))\n",
        "\n",
        "# Register button click event handlers\n",
        "button_yes.on_click(on_button_yes_clicked)\n",
        "button_no.on_click(on_button_no_clicked)\n",
        "button_train.on_click(on_button_train_clicked)\n",
        "\n",
        "# Set initial widget visibility\n",
        "input_num_epochs.layout.display = 'none'\n",
        "button_train.layout.display = 'none'\n",
        "\n",
        "# Display the widgets\n",
        "display(label_train_central)\n",
        "display(button_yes)\n",
        "display(button_no)\n",
        "display(input_num_epochs)\n",
        "display(button_train)\n",
        "display(output_train_epoch)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "message = '<h3 style=\"color:red; width:100px;\">NEXT STEP</h3>'\n",
        "display(HTML(message))\n",
        "cen = '<h1 style=\"color:blue; width:100px;\">FEDRATED_LEARNIN_WITH_FLOWER</h1>'\n",
        "display(HTML(cen))  \n",
        "\n",
        "\n",
        "\n",
        "def get_parameters(net) -> List[np.ndarray]:\n",
        "    return [val.cpu().numpy() for _, val in net.state_dict().items()]\n",
        "\n",
        "def set_parameters(net, parameters: List[np.ndarray]):\n",
        "    params_dict = zip(net.state_dict().keys(), parameters)\n",
        "    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n",
        "    net.load_state_dict(state_dict, strict=True)\n",
        "\n",
        "class FlowerClient(fl.client.NumPyClient):\n",
        "    def __init__(self, net, trainloader, valloader):\n",
        "        self.net = net\n",
        "        self.trainloader = trainloader\n",
        "        self.valloader = valloader\n",
        "\n",
        "    def get_parameters(self, config):\n",
        "        return get_parameters(self.net)\n",
        "\n",
        "    def fit(self, parameters, config):\n",
        "        set_parameters(self.net, parameters)\n",
        "        train(self.net, self.trainloader, epochs=1)\n",
        "        return get_parameters(self.net), len(self.trainloader), {}\n",
        "\n",
        "    def evaluate(self, parameters, config):\n",
        "        set_parameters(self.net, parameters)\n",
        "        loss, accuracy = test(self.net, self.valloader)\n",
        "        return float(loss), len(self.valloader), {\"accuracy\": float(accuracy)}\n",
        "\n",
        "def aggregate_FedAdagrad(metrics: List[Tuple[int, dict]]) -> dict:\n",
        "    total_examples = sum([num_examples for num_examples, _ in metrics])\n",
        "    weighted_sum = {key: np.zeros_like(metrics[0][1][key]) for key in metrics[0][1]}  # Initialize weighted sum with zeros\n",
        "\n",
        "    for num_examples, metric in metrics:\n",
        "        for key in metric:\n",
        "            # Multiply each metric value by the number of examples and add to the weighted sum\n",
        "            weighted_sum[key] += num_examples * metric[key]\n",
        "\n",
        "    # Divide the weighted sum by the total number of examples to compute the weighted average\n",
        "    weighted_avg = {key: weighted_sum[key] / total_examples for key in weighted_sum}\n",
        "\n",
        "    return weighted_avg\n",
        "\n",
        "penalty_weight = 0.1 \n",
        "def aggregate_FedProx(metrics: List[Tuple[int, dict]]) -> dict:\n",
        "    accuracies = [num_examples * m[\"accuracy\"] for num_examples, m in metrics]\n",
        "    examples = [num_examples for num_examples, _ in metrics]\n",
        "\n",
        "    # Aggregate accuracy and examples\n",
        "    aggregated_metrics = {\n",
        "        \"accuracy\": sum(accuracies) / sum(examples),\n",
        "        \"examples\": sum(examples)\n",
        "    }\n",
        "\n",
        "    # Apply penalty term to the aggregated accuracy\n",
        "    aggregated_metrics[\"accuracy_with_penalty\"] = aggregated_metrics[\"accuracy\"] + penalty_weight * aggregated_metrics[\"examples\"]\n",
        "\n",
        "    return aggregated_metrics\n",
        "\n",
        "def average_FedAvg(metrics: List[Tuple[int, dict]]) -> dict:\n",
        "    # Multiply accuracy of each client by number of examples used\n",
        "    accuracies = [num_examples * m[\"accuracy\"] for num_examples, m in metrics]\n",
        "    examples = [num_examples for num_examples, _ in metrics]\n",
        "\n",
        "    # Aggregate and return custom metric (weighted average)\n",
        "    return {\"accuracy\": sum(accuracies) / sum(examples)}\n",
        "\n",
        "def aggregation_FedAdam(metrics: List[Tuple[int, dict]]) -> dict:\n",
        "    # Multiply accuracy of each client by number of examples used\n",
        "    accuracies = [num_examples * m[\"accuracy\"] for num_examples, m in metrics]\n",
        "    examples = [num_examples for num_examples, _ in metrics]\n",
        "\n",
        "    # Aggregate and return custom metric (weighted average)\n",
        "    total_examples = sum(examples)\n",
        "    weighted_acc_sum = sum(accuracies)\n",
        "\n",
        "    # Apply FedAdam strategy (e.g., update the weighted average with global update)\n",
        "    global_update = 0.1  # Replace with the appropriate global update value\n",
        "\n",
        "    weighted_acc_sum += global_update * total_examples\n",
        "    total_examples += global_update\n",
        "\n",
        "    return {\"accuracy\": weighted_acc_sum / total_examples}\n",
        "\n",
        "#Using the Virtual Client Engine\n",
        "def client_fn(cid: str) -> FlowerClient:\n",
        "    \"\"\"Create a Flower client representing a single organization.\"\"\"\n",
        "\n",
        "    # Load model\n",
        "    net = Net(num_c).to(DEVICE)\n",
        "\n",
        "    # Load data (CIFAR-10)\n",
        "    # Note: each client gets a different trainloader/valloader, so each client\n",
        "    # will train and evaluate on their own unique data\n",
        "    trainloader = trainloaders[int(cid)]\n",
        "    valloader = valloaders[int(cid)]\n",
        "\n",
        "    # Create a single Flower client representing a single organization\n",
        "    return FlowerClient(net, trainloader, valloader)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Define num_round_input dropdown\n",
        "num_round_input = widgets.Dropdown(\n",
        "    options=[1, 2, 5, 10, 100],\n",
        "   description=\"N_Rounds:\"\n",
        "\n",
        "\n",
        ")\n",
        "\n",
        "# Define strategy dropdown\n",
        "strategy_dropdown = widgets.Dropdown(\n",
        "    options=[\"FedAvg\", \"FedAdam\", \"FedProx\", \"FedAdagrad\"],\n",
        "    description=\"Strategy:\"\n",
        " \n",
        ")\n",
        "\n",
        "# Define button\n",
        "run_button = widgets.Button(description=\"Run Simulation\")\n",
        "\n",
        "# Define output text area\n",
        "output_text = widgets.Output()\n",
        "\n",
        "# Define function to run simulation on button click\n",
        "def run_simulation(button):\n",
        "    num_round = num_round_input.value\n",
        "    \n",
        "    if strategy_dropdown.value == \"FedAvg\":\n",
        "        strategy = fl.server.strategy.FedAvg(\n",
        "            fraction_fit=1.0,\n",
        "            fraction_evaluate=0.5,\n",
        "            min_fit_clients=num_clients - 1,\n",
        "            min_evaluate_clients=num_clients - 2,\n",
        "            min_available_clients=num_clients,\n",
        "            initial_parameters=fl.common.ndarrays_to_parameters(get_parameters(Net(num_c))),\n",
        "            evaluate_metrics_aggregation_fn=average_FedAvg,  # <-- pass the metric aggregation function\n",
        "        )\n",
        "        # Start simulation with FedAvg strategy\n",
        "    elif strategy_dropdown.value == \"FedAdagrad\":\n",
        "        # Start simulation with FedAdagrad strategy\n",
        "        from flwr.server.strategy import FedAdagrad\n",
        "        # Create FedAdagrad strategy\n",
        "        strategy = FedAdagrad(\n",
        "            fraction_fit=1.0,\n",
        "            fraction_evaluate=0.5,\n",
        "            min_fit_clients=num_clients - 1,\n",
        "            min_evaluate_clients=num_clients - 2,\n",
        "            min_available_clients=num_clients,\n",
        "            initial_parameters=fl.common.ndarrays_to_parameters(get_parameters(Net(num_c))),\n",
        "            evaluate_metrics_aggregation_fn=aggregate_FedAdagrad,  \n",
        "        )\n",
        "    elif strategy_dropdown.value == \"FedProx\":\n",
        "        # Start simulation with FedProx strategy\n",
        "        from flwr.server.strategy.fedprox import FedProx\n",
        "        strategy = FedProx(\n",
        "            fraction_fit=1.0,\n",
        "            fraction_evaluate=0.5,\n",
        "            min_fit_clients=num_clients - 1,\n",
        "            min_evaluate_clients=num_clients - 2,\n",
        "            min_available_clients=num_clients,\n",
        "            evaluate_metrics_aggregation_fn=aggregate_FedProx,\n",
        "            initial_parameters=fl.common.ndarrays_to_parameters(get_parameters(Net(num_c))),\n",
        "            proximal_mu=0.1,\n",
        "        )\n",
        "    elif strategy_dropdown.value == \"FedAdam\":\n",
        "        # Start simulation with FedAdam strategy\n",
        "        from flwr.server.strategy import FedAdam\n",
        "        strategy = FedAdam(\n",
        "            fraction_fit=1.0,\n",
        "            fraction_evaluate=0.5,\n",
        "            min_fit_clients=num_clients - 1,\n",
        "            min_evaluate_clients=num_clients - 2,\n",
        "            min_available_clients=num_clients,\n",
        "            evaluate_metrics_aggregation_fn=aggregation_FedAdam,\n",
        "            initial_parameters=fl.common.ndarrays_to_parameters(get_parameters(Net(num_c))),\n",
        "            eta=1e-1,  # Adjust the learning rate if needed\n",
        "            eta_l=1e-1,  # Adjust the client-side learning rate if needed\n",
        "            beta_1=0.9,  # Adjust the momentum parameter if needed\n",
        "            beta_2=0.99,  # Adjust the second moment parameter if needed\n",
        "            tau=1e-9,\n",
        "        )\n",
        "    else:\n",
        "        # Run simulation using other strategy\n",
        "        # Add code for the other strategy here\n",
        "        pass\n",
        "\n",
        "    # Update with your specific simulation code\n",
        "    \n",
        "    output = fl.simulation.start_simulation(\n",
        "        client_fn=client_fn,\n",
        "        num_clients=num_clients,\n",
        "        config=fl.server.ServerConfig(num_rounds=num_round),\n",
        "        strategy=strategy,\n",
        "        client_resources=client_resources,\n",
        "    )\n",
        "\n",
        "    # Print output\n",
        "    with output_text:\n",
        "        print(output)\n",
        "\n",
        "# Add button click event handler\n",
        "run_button.on_click(run_simulation)\n",
        "\n",
        "# Display widgets\n",
        "display(widgets.VBox([num_round_input, strategy_dropdown, run_button, output_text]))\n",
        "\n",
        "message = '<h3 style=\"color:red; width:100px;\">THE_END</h3>'\n",
        "display(HTML(message))\n",
        "cen = '<h1 style=\"color:blue; width:100px;\">THE_OUTPUT_CLASSES_OF_DATASET</h1>'\n",
        "display(HTML(cen))  "
      ]
    }
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