federated_test1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "nuclear-concrete",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import random\n",
    "import math\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from matplotlib import pyplot\n",
    "from pathlib import Path\n",
    "import requests\n",
    "import pickle\n",
    "import gzip\n",
    "import torch\n",
    "import math\n",
    "import torch.nn.functional as F\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import TensorDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from keras.datasets import mnist\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "coordinated-brighton",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "killing-leader",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, \n",
    "    test_size=0.2, random_state= 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "empty-cycling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "784\n"
     ]
    }
   ],
   "source": [
    "image_size = x_train.shape[1]\n",
    "input_size = image_size * image_size\n",
    "print(input_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "automated-cannon",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.reshape(x_train, [-1, input_size])\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_valid = np.reshape(x_valid, [-1, input_size])\n",
    "x_valid = x_valid.astype('float32') / 255\n",
    "x_test = np.reshape(x_test, [-1, input_size])\n",
    "x_test = x_test.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "common-moscow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((48000, 784), (48000,), (12000, 784), (12000,), (10000, 784), (10000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape , x_valid.shape, y_valid.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "quiet-handbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "frequent-safety",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = pyplot.subplots(8,8,figsize=(8,8))\n",
    "for i in range(8):\n",
    "    for j in range(8):\n",
    "        num_index = np.random.randint(len(x_train))\n",
    "        axes[i,j].imshow(x_train[num_index].reshape((28,28)), cmap=\"gray\")\n",
    "        axes[i,j].axis(\"off\")\n",
    "pyplot.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "received-burner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 >> train: 4742 , valid: 1181 , test: 980 , total: 6903\n",
      "1 >> train: 5314 , valid: 1428 , test: 1135 , total: 7877\n",
      "2 >> train: 4798 , valid: 1160 , test: 1032 , total: 6990\n",
      "3 >> train: 4883 , valid: 1248 , test: 1010 , total: 7141\n",
      "4 >> train: 4692 , valid: 1150 , test: 982 , total: 6824\n",
      "5 >> train: 4333 , valid: 1088 , test: 892 , total: 6313\n",
      "6 >> train: 4776 , valid: 1142 , test: 958 , total: 6876\n",
      "7 >> train: 5027 , valid: 1238 , test: 1028 , total: 7293\n",
      "8 >> train: 4674 , valid: 1177 , test: 974 , total: 6825\n",
      "9 >> train: 4761 , valid: 1188 , test: 1009 , total: 6958\n",
      "y_train_total= 48000\n",
      "y_valid_total= 12000\n",
      "y_test_total= 10000\n",
      "total= 70000\n"
     ]
    }
   ],
   "source": [
    "# Let's check how many of each tag are.\n",
    "y_train_total=0\n",
    "y_valid_total=0\n",
    "y_test_total=0\n",
    "total=0\n",
    "for i in range(10):\n",
    "    print(i,\">> train:\", sum(y_train==i), \", valid:\", sum(y_valid==i), \n",
    "          \", test:\", sum(y_test==i), \", total:\", sum(y_train==i)+sum(y_valid==i)+sum(y_test==i) )\n",
    "    y_train_total=y_train_total + sum(y_train==i)\n",
    "    y_valid_total=y_valid_total + sum(y_valid==i)\n",
    "    y_test_total=y_test_total + sum(y_test==i)\n",
    "    total=total+sum(y_train==i)+sum(y_valid==i)+sum(y_test==i)\n",
    "    \n",
    "print(\"y_train_total=\", y_train_total) \n",
    "print(\"y_valid_total=\", y_valid_total) \n",
    "print(\"y_test_total=\", y_test_total)\n",
    "print(\"total=\", total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fitting-minutes",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_and_shuffle_labels(y_data, seed, amount):\n",
    "    y_data=pd.DataFrame(y_data,columns=[\"labels\"])\n",
    "    y_data[\"i\"]=np.arange(len(y_data))\n",
    "    label_dict = dict()\n",
    "    for i in range(10):\n",
    "        var_name=\"label\" + str(i)\n",
    "        label_info=y_data[y_data[\"labels\"]==i]\n",
    "        np.random.seed(seed)\n",
    "        label_info=np.random.permutation(label_info)\n",
    "        label_info=label_info[0:amount]\n",
    "        label_info=pd.DataFrame(label_info, columns=[\"labels\",\"i\"])\n",
    "        label_dict.update({var_name: label_info })\n",
    "    return label_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "radio-idaho",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_iid_subsamples_indices(label_dict, number_of_samples, amount):\n",
    "    sample_dict= dict()\n",
    "    batch_size=int(math.floor(amount/number_of_samples))\n",
    "    for i in range(number_of_samples):\n",
    "        sample_name=\"sample\"+str(i)\n",
    "        dumb=pd.DataFrame()\n",
    "        for j in range(10):\n",
    "            label_name=str(\"label\")+str(j)\n",
    "            a=label_dict[label_name][i*batch_size:(i+1)*batch_size]\n",
    "            dumb=pd.concat([dumb,a], axis=0)\n",
    "        dumb.reset_index(drop=True, inplace=True)    \n",
    "        sample_dict.update({sample_name: dumb}) \n",
    "    return sample_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sorted-friday",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_iid_subsamples(sample_dict, x_data, y_data, x_name, y_name):\n",
    "    x_data_dict= dict()\n",
    "    y_data_dict= dict()\n",
    "    \n",
    "    for i in range(len(sample_dict)):  ### len(sample_dict)= number of samples\n",
    "        xname= x_name+str(i)\n",
    "        yname= y_name+str(i)\n",
    "        sample_name=\"sample\"+str(i)\n",
    "        \n",
    "        indices=np.sort(np.array(sample_dict[sample_name][\"i\"]))\n",
    "        \n",
    "        x_info= x_data[indices,:]\n",
    "        x_data_dict.update({xname : x_info})\n",
    "        \n",
    "        y_info= y_data[indices]\n",
    "        y_data_dict.update({yname : y_info})\n",
    "        \n",
    "    return x_data_dict, y_data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "hindu-colleague",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net2nn(nn.Module):\n",
    "    def __init__(self,n_features):\n",
    "        super(Net2nn, self).__init__()\n",
    "        self.fc1=nn.Linear(n_features,200)\n",
    "        self.fc2=nn.Linear(200,200)\n",
    "        self.fc3=nn.Linear(200,10)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        x=F.relu(self.fc1(x))\n",
    "        x=F.relu(self.fc2(x))\n",
    "        x=self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "rental-improvement",
   "metadata": {},
   "outputs": [],
   "source": [
    "class WrappedDataLoader:\n",
    "    def __init__(self, dl, func):\n",
    "        self.dl = dl\n",
    "        self.func = func\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dl)\n",
    "\n",
    "    def __iter__(self):\n",
    "        batches = iter(self.dl)\n",
    "        for b in batches:\n",
    "            yield (self.func(*b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fifth-tactics",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, criterion, optimizer):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    correct = 0\n",
    "\n",
    "    for data, target in train_loader:\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target.long())\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item()\n",
    "        prediction = output.argmax(dim=1, keepdim=True)\n",
    "        correct += prediction.eq(target.view_as(prediction)).sum().item()\n",
    "        \n",
    "\n",
    "    return train_loss / len(train_loader), correct/len(train_loader.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "boring-necessity",
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(model, test_loader, criterion):\n",
    "    model.eval()\n",
    "    test_loss = 0.0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            output = model(data)\n",
    "            \n",
    "            test_loss += criterion(output, target.long()).item()\n",
    "            prediction = output.argmax(dim=1, keepdim=True)\n",
    "            correct += prediction.eq(target.view_as(prediction)).sum().item()\n",
    "\n",
    "    test_loss /= len(test_loader)\n",
    "    correct /= len(test_loader.dataset)\n",
    "\n",
    "    return (test_loss, correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "oriented-landscape",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model_optimizer_criterion_dict(number_of_samples):\n",
    "    model_dict = dict()\n",
    "    optimizer_dict= dict()\n",
    "    criterion_dict = dict()\n",
    "    \n",
    "    for i in range(number_of_samples):\n",
    "        model_name=\"model\"+str(i)\n",
    "        model_info=Net2nn(x_train.shape[1])\n",
    "        model_dict.update({model_name : model_info })\n",
    "        \n",
    "        optimizer_name=\"optimizer\"+str(i)\n",
    "        optimizer_info = torch.optim.SGD(model_info.parameters(), lr=learning_rate, momentum=momentum)\n",
    "        optimizer_dict.update({optimizer_name : optimizer_info })\n",
    "        \n",
    "        criterion_name = \"criterion\"+str(i)\n",
    "        criterion_info = nn.CrossEntropyLoss()\n",
    "        criterion_dict.update({criterion_name : criterion_info})\n",
    "        \n",
    "    return model_dict, optimizer_dict, criterion_dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "classified-table",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_averaged_weights(model_dict, number_of_samples):\n",
    "   \n",
    "    fc1_mean_weight = torch.zeros(size=model_dict[name_of_models[0]].fc1.weight.shape)\n",
    "    fc1_mean_bias = torch.zeros(size=model_dict[name_of_models[0]].fc1.bias.shape)\n",
    "    \n",
    "    fc2_mean_weight = torch.zeros(size=model_dict[name_of_models[0]].fc2.weight.shape)\n",
    "    fc2_mean_bias = torch.zeros(size=model_dict[name_of_models[0]].fc2.bias.shape)\n",
    "    \n",
    "    fc3_mean_weight = torch.zeros(size=model_dict[name_of_models[0]].fc3.weight.shape)\n",
    "    fc3_mean_bias = torch.zeros(size=model_dict[name_of_models[0]].fc3.bias.shape)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "    \n",
    "        for i in range(number_of_samples):\n",
    "            fc1_mean_weight += model_dict[name_of_models[i]].fc1.weight.data.clone()\n",
    "            fc1_mean_bias += model_dict[name_of_models[i]].fc1.bias.data.clone()\n",
    "        \n",
    "            fc2_mean_weight += model_dict[name_of_models[i]].fc2.weight.data.clone()\n",
    "            fc2_mean_bias += model_dict[name_of_models[i]].fc2.bias.data.clone()\n",
    "        \n",
    "            fc3_mean_weight += model_dict[name_of_models[i]].fc3.weight.data.clone()\n",
    "            fc3_mean_bias += model_dict[name_of_models[i]].fc3.bias.data.clone()\n",
    "\n",
    "        \n",
    "        fc1_mean_weight =fc1_mean_weight/number_of_samples\n",
    "        fc1_mean_bias = fc1_mean_bias/ number_of_samples\n",
    "    \n",
    "        fc2_mean_weight =fc2_mean_weight/number_of_samples\n",
    "        fc2_mean_bias = fc2_mean_bias/ number_of_samples\n",
    "    \n",
    "        fc3_mean_weight =fc3_mean_weight/number_of_samples\n",
    "        fc3_mean_bias = fc3_mean_bias/ number_of_samples\n",
    "    \n",
    "    return fc1_mean_weight, fc1_mean_bias, fc2_mean_weight, fc2_mean_bias, fc3_mean_weight, fc3_mean_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "forced-expression",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_averaged_weights_as_main_model_weights_and_update_main_model(main_model,model_dict, number_of_samples):\n",
    "    fc1_mean_weight, fc1_mean_bias, fc2_mean_weight, fc2_mean_bias, fc3_mean_weight, fc3_mean_bias = get_averaged_weights(model_dict, number_of_samples=number_of_samples)\n",
    "    with torch.no_grad():\n",
    "        main_model.fc1.weight.data = fc1_mean_weight.data.clone()\n",
    "        main_model.fc2.weight.data = fc2_mean_weight.data.clone()\n",
    "        main_model.fc3.weight.data = fc3_mean_weight.data.clone()\n",
    "\n",
    "        main_model.fc1.bias.data = fc1_mean_bias.data.clone()\n",
    "        main_model.fc2.bias.data = fc2_mean_bias.data.clone()\n",
    "        main_model.fc3.bias.data = fc3_mean_bias.data.clone() \n",
    "    return main_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "monthly-domain",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_local_and_merged_model_performance(number_of_samples):\n",
    "    accuracy_table=pd.DataFrame(data=np.zeros((number_of_samples,3)), columns=[\"sample\", \"local_ind_model\", \"merged_main_model\"])\n",
    "    for i in range (number_of_samples):\n",
    "    \n",
    "        test_ds = TensorDataset(x_test_dict[name_of_x_test_sets[i]], y_test_dict[name_of_y_test_sets[i]])\n",
    "        test_dl = DataLoader(test_ds, batch_size=batch_size * 2)\n",
    "    \n",
    "        model=model_dict[name_of_models[i]]\n",
    "        criterion=criterion_dict[name_of_criterions[i]]\n",
    "        optimizer=optimizer_dict[name_of_optimizers[i]]\n",
    "    \n",
    "        individual_loss, individual_accuracy = validation(model, test_dl, criterion)\n",
    "        main_loss, main_accuracy =validation(main_model, test_dl, main_criterion )\n",
    "    \n",
    "        accuracy_table.loc[i, \"sample\"]=\"sample \"+str(i)\n",
    "        accuracy_table.loc[i, \"local_ind_model\"] = individual_accuracy\n",
    "        accuracy_table.loc[i, \"merged_main_model\"] = main_accuracy\n",
    "\n",
    "    return accuracy_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "superior-shanghai",
   "metadata": {},
   "outputs": [],
   "source": [
    "def send_main_model_to_nodes_and_update_model_dict(main_model, model_dict, number_of_samples):\n",
    "    with torch.no_grad():\n",
    "        for i in range(number_of_samples):\n",
    "\n",
    "            model_dict[name_of_models[i]].fc1.weight.data =main_model.fc1.weight.data.clone()\n",
    "            model_dict[name_of_models[i]].fc2.weight.data =main_model.fc2.weight.data.clone()\n",
    "            model_dict[name_of_models[i]].fc3.weight.data =main_model.fc3.weight.data.clone() \n",
    "            \n",
    "            model_dict[name_of_models[i]].fc1.bias.data =main_model.fc1.bias.data.clone()\n",
    "            model_dict[name_of_models[i]].fc2.bias.data =main_model.fc2.bias.data.clone()\n",
    "            model_dict[name_of_models[i]].fc3.bias.data =main_model.fc3.bias.data.clone() \n",
    "    \n",
    "    return model_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "motivated-crossing",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_train_end_node_process(number_of_samples):\n",
    "    for i in range (number_of_samples): \n",
    "\n",
    "        train_ds = TensorDataset(x_train_dict[name_of_x_train_sets[i]], y_train_dict[name_of_y_train_sets[i]])\n",
    "        train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "#         valid_ds = TensorDataset(x_valid_dict[name_of_x_valid_sets[i]], y_valid_dict[name_of_y_valid_sets[i]])\n",
    "#         valid_dl = DataLoader(valid_ds, batch_size=batch_size * 2)\n",
    "        \n",
    "        test_ds = TensorDataset(x_test_dict[name_of_x_test_sets[i]], y_test_dict[name_of_y_test_sets[i]])\n",
    "        test_dl = DataLoader(test_ds, batch_size= batch_size * 2)\n",
    "    \n",
    "        model=model_dict[name_of_models[i]]\n",
    "        criterion=criterion_dict[name_of_criterions[i]]\n",
    "        optimizer=optimizer_dict[name_of_optimizers[i]]\n",
    "    \n",
    "        print(\"Subset\" ,i)\n",
    "        for epoch in range(numEpoch):        \n",
    "            train_loss, train_accuracy = train(model, train_dl, criterion, optimizer)\n",
    "#             valid_loss, valid_accuracy = validation(model, valid_dl, criterion)\n",
    "            test_loss, test_accuracy = validation(model, test_dl, criterion)\n",
    "    \n",
    "            print(\"epoch: {:3.0f}\".format(epoch+1) + \" | train accuracy: {:7.5f}\".format(train_accuracy) + \" | test accuracy: {:7.5f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "wicked-mixture",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_train_end_node_process_without_print(number_of_samples):\n",
    "    for i in range (number_of_samples): \n",
    "\n",
    "        train_ds = TensorDataset(x_train_dict[name_of_x_train_sets[i]], y_train_dict[name_of_y_train_sets[i]])\n",
    "        train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "        test_ds = TensorDataset(x_test_dict[name_of_x_test_sets[i]], y_test_dict[name_of_y_test_sets[i]])\n",
    "        test_dl = DataLoader(test_ds, batch_size= batch_size * 2)\n",
    "    \n",
    "        model=model_dict[name_of_models[i]]\n",
    "        criterion=criterion_dict[name_of_criterions[i]]\n",
    "        optimizer=optimizer_dict[name_of_optimizers[i]]\n",
    "    \n",
    "        for epoch in range(numEpoch):        \n",
    "            train_loss, train_accuracy = train(model, train_dl, criterion, optimizer)\n",
    "            test_loss, test_accuracy = validation(model, test_dl, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ranging-giving",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_train_end_node_process_print_some(number_of_samples, print_amount):\n",
    "    for i in range (number_of_samples): \n",
    "\n",
    "        train_ds = TensorDataset(x_train_dict[name_of_x_train_sets[i]], y_train_dict[name_of_y_train_sets[i]])\n",
    "        train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "        test_ds = TensorDataset(x_test_dict[name_of_x_test_sets[i]], y_test_dict[name_of_y_test_sets[i]])\n",
    "        test_dl = DataLoader(test_ds, batch_size= batch_size * 2)\n",
    "    \n",
    "        model=model_dict[name_of_models[i]]\n",
    "        criterion=criterion_dict[name_of_criterions[i]]\n",
    "        optimizer=optimizer_dict[name_of_optimizers[i]]\n",
    "    \n",
    "        if i<print_amount:\n",
    "            print(\"Subset\" ,i)\n",
    "            \n",
    "        for epoch in range(numEpoch):\n",
    "        \n",
    "            train_loss, train_accuracy = train(model, train_dl, criterion, optimizer)\n",
    "            test_loss, test_accuracy = validation(model, test_dl, criterion)\n",
    "            \n",
    "            if i<print_amount:        \n",
    "                print(\"epoch: {:3.0f}\".format(epoch+1) + \" | train accuracy: {:7.5f}\".format(train_accuracy) + \" | test accuracy: {:7.5f}\".format(test_accuracy))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "stunning-front",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_valid, y_valid,x_test, y_test = map(torch.tensor, (x_train, y_train, x_valid, y_valid, x_test, y_test))\n",
    "number_of_samples=100\n",
    "learning_rate = 0.01\n",
    "numEpoch = 10\n",
    "batch_size = 32\n",
    "momentum = 0.9\n",
    "\n",
    "train_amount=4500\n",
    "valid_amount=900\n",
    "test_amount=900\n",
    "print_amount=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "sealed-evolution",
   "metadata": {},
   "outputs": [],
   "source": [
    "centralized_model = Net2nn(x_train.shape[1])\n",
    "centralized_optimizer = torch.optim.SGD(centralized_model.parameters(), lr=0.01, momentum=0.9)\n",
    "centralized_criterion = nn.CrossEntropyLoss()\n",
    "#centralized_criterion = nn.BCEWithLogitsLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "auburn-poker",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = TensorDataset(x_train, y_train)\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "valid_ds = TensorDataset(x_valid, y_valid)\n",
    "valid_dl = DataLoader(valid_ds, batch_size=batch_size * 2)\n",
    "\n",
    "test_ds = TensorDataset(x_test, y_test)\n",
    "test_dl = DataLoader(test_ds, batch_size=batch_size * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "round-tackle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Centralized Model ------\n",
      "epoch:   1 | train accuracy:  0.8773 | test accuracy:  0.9477\n",
      "epoch:   2 | train accuracy:  0.9564 | test accuracy:  0.9636\n",
      "epoch:   3 | train accuracy:  0.9705 | test accuracy:  0.9699\n",
      "epoch:   4 | train accuracy:  0.9781 | test accuracy:  0.9725\n",
      "epoch:   5 | train accuracy:  0.9830 | test accuracy:  0.9738\n",
      "epoch:   6 | train accuracy:  0.9865 | test accuracy:  0.9757\n",
      "epoch:   7 | train accuracy:  0.9899 | test accuracy:  0.9757\n",
      "epoch:   8 | train accuracy:  0.9920 | test accuracy:  0.9800\n",
      "epoch:   9 | train accuracy:  0.9935 | test accuracy:  0.9755\n",
      "epoch:  10 | train accuracy:  0.9963 | test accuracy:  0.9788\n",
      "------ Training finished ------\n"
     ]
    }
   ],
   "source": [
    "print(\"------ Centralized Model ------\")\n",
    "for epoch in range(numEpoch):\n",
    "    central_train_loss, central_train_accuracy = train(centralized_model, train_dl, centralized_criterion, centralized_optimizer)\n",
    "    central_test_loss, central_test_accuracy = validation(centralized_model, test_dl, centralized_criterion)\n",
    "    \n",
    "    print(\"epoch: {:3.0f}\".format(epoch+1) + \" | train accuracy: {:7.4f}\".format(central_train_accuracy) + \" | test accuracy: {:7.4f}\".format(central_test_accuracy))\n",
    "\n",
    "print(\"------ Training finished ------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-venezuela",
   "metadata": {},
   "source": [
    "Data is distributed to nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "developmental-publisher",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_dict_train=split_and_shuffle_labels(y_data=y_train, seed=1, amount=train_amount) \n",
    "sample_dict_train=get_iid_subsamples_indices(label_dict=label_dict_train, number_of_samples=number_of_samples, amount=train_amount)\n",
    "x_train_dict, y_train_dict = create_iid_subsamples(sample_dict=sample_dict_train, x_data=x_train, y_data=y_train, x_name=\"x_train\", y_name=\"y_train\")\n",
    "\n",
    "\n",
    "label_dict_valid = split_and_shuffle_labels(y_data=y_valid, seed=1, amount=train_amount) \n",
    "sample_dict_valid = get_iid_subsamples_indices(label_dict=label_dict_valid, number_of_samples=number_of_samples, amount=valid_amount)\n",
    "x_valid_dict, y_valid_dict = create_iid_subsamples(sample_dict=sample_dict_valid, x_data=x_valid, y_data=y_valid, x_name=\"x_valid\", y_name=\"y_valid\")\n",
    "\n",
    "label_dict_test = split_and_shuffle_labels(y_data=y_test, seed=1, amount=test_amount) \n",
    "sample_dict_test = get_iid_subsamples_indices(label_dict=label_dict_test, number_of_samples=number_of_samples, amount=test_amount)\n",
    "x_test_dict, y_test_dict = create_iid_subsamples(sample_dict=sample_dict_test, x_data=x_test, y_data=y_test, x_name=\"x_test\", y_name=\"y_test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "standard-receipt",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([450, 784]) torch.Size([450])\n",
      "torch.Size([90, 784]) torch.Size([90])\n",
      "torch.Size([90, 784]) torch.Size([90])\n",
      "tensor(1, dtype=torch.uint8)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(x_train_dict[\"x_train1\"].shape, y_train_dict[\"y_train1\"].shape)\n",
    "print(x_valid_dict[\"x_valid1\"].shape, y_valid_dict[\"y_valid1\"].shape) \n",
    "print(x_test_dict[\"x_test1\"].shape, y_test_dict[\"y_test1\"].shape)\n",
    "\n",
    "num_index = np.random.randint(test_amount/number_of_samples*10)\n",
    "pyplot.imshow(x_test_dict[\"x_test0\"][num_index].reshape((28,28)), cmap=\"gray\")\n",
    "print(y_test_dict[\"y_test0\"][num_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "characteristic-burden",
   "metadata": {},
   "source": [
    "Main model is created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "proper-computer",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_model = Net2nn(x_train.shape[1])\n",
    "main_optimizer = torch.optim.SGD(main_model.parameters(), lr=learning_rate, momentum=0.9)\n",
    "main_criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automated-complaint",
   "metadata": {},
   "source": [
    "Models,optimizers and loss functions in nodes are defined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "typical-professor",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict, optimizer_dict, criterion_dict = create_model_optimizer_criterion_dict(number_of_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rapid-london",
   "metadata": {},
   "source": [
    "Keys of dicts are being made iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "collect-venezuela",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_of_x_train_sets=list(x_train_dict.keys())\n",
    "name_of_y_train_sets=list(y_train_dict.keys())\n",
    "name_of_x_valid_sets=list(x_valid_dict.keys())\n",
    "name_of_y_valid_sets=list(y_valid_dict.keys())\n",
    "name_of_x_test_sets=list(x_test_dict.keys())\n",
    "name_of_y_test_sets=list(y_test_dict.keys())\n",
    "\n",
    "name_of_models=list(model_dict.keys())\n",
    "name_of_optimizers=list(optimizer_dict.keys())\n",
    "name_of_criterions=list(criterion_dict.keys())\n",
    "\n",
    "# print(name_of_x_train_sets)\n",
    "# print(name_of_y_train_sets)\n",
    "# print(name_of_x_valid_sets)\n",
    "# print(name_of_y_valid_sets)\n",
    "# print(name_of_x_test_sets)\n",
    "# print(name_of_y_test_sets)\n",
    "# print(\"\\n ------------\")\n",
    "# print(name_of_models)\n",
    "# print(name_of_optimizers)\n",
    "# print(name_of_criterions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cubic-preliminary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.0705, -0.0664,  0.0337, -0.0174, -0.0052]],\n",
      "       grad_fn=<SliceBackward>)\n",
      "tensor([[ 0.0301, -0.0172, -0.0201, -0.0394,  0.0088]],\n",
      "       grad_fn=<SliceBackward>)\n"
     ]
    }
   ],
   "source": [
    "print(main_model.fc2.weight[0:1,0:5])\n",
    "print(model_dict[\"model1\"].fc2.weight[0:1,0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "essential-stephen",
   "metadata": {},
   "source": [
    "Parameters of main model are sent to nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "physical-beast",
   "metadata": {},
   "source": [
    "Since the parameters of the main model and parameters of all local models in the nodes are randomly initialized, all these parameters will be different from each other. For this reason, the main model sends its parameters to the nodes before the training of local models in the nodes begins. You can check the weights below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "vietnamese-chile",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict=send_main_model_to_nodes_and_update_model_dict(main_model, model_dict, number_of_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "revolutionary-operations",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.0705, -0.0664,  0.0337, -0.0174, -0.0052]],\n",
      "       grad_fn=<SliceBackward>)\n",
      "tensor([[ 0.0705, -0.0664,  0.0337, -0.0174, -0.0052]],\n",
      "       grad_fn=<SliceBackward>)\n"
     ]
    }
   ],
   "source": [
    "print(main_model.fc2.weight[0:1,0:5])\n",
    "print(model_dict[\"model1\"].fc2.weight[0:1,0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reflected-brazil",
   "metadata": {},
   "source": [
    "Models in the nodes are trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "nasty-professor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subset 0\n",
      "epoch:   1 | train accuracy: 0.10000 | test accuracy: 0.13333\n",
      "epoch:   2 | train accuracy: 0.29333 | test accuracy: 0.44444\n",
      "epoch:   3 | train accuracy: 0.53111 | test accuracy: 0.54444\n",
      "epoch:   4 | train accuracy: 0.48667 | test accuracy: 0.60000\n",
      "epoch:   5 | train accuracy: 0.67333 | test accuracy: 0.67778\n",
      "epoch:   6 | train accuracy: 0.63111 | test accuracy: 0.70000\n",
      "epoch:   7 | train accuracy: 0.71333 | test accuracy: 0.76667\n",
      "epoch:   8 | train accuracy: 0.80889 | test accuracy: 0.81111\n",
      "epoch:   9 | train accuracy: 0.84000 | test accuracy: 0.76667\n",
      "epoch:  10 | train accuracy: 0.87111 | test accuracy: 0.80000\n",
      "Subset 1\n",
      "epoch:   1 | train accuracy: 0.10000 | test accuracy: 0.10000\n",
      "epoch:   2 | train accuracy: 0.35111 | test accuracy: 0.38889\n",
      "epoch:   3 | train accuracy: 0.47333 | test accuracy: 0.48889\n",
      "epoch:   4 | train accuracy: 0.55111 | test accuracy: 0.61111\n",
      "epoch:   5 | train accuracy: 0.60222 | test accuracy: 0.60000\n",
      "epoch:   6 | train accuracy: 0.61333 | test accuracy: 0.62222\n",
      "epoch:   7 | train accuracy: 0.66667 | test accuracy: 0.60000\n",
      "epoch:   8 | train accuracy: 0.80222 | test accuracy: 0.72222\n",
      "epoch:   9 | train accuracy: 0.79778 | test accuracy: 0.66667\n",
      "epoch:  10 | train accuracy: 0.85111 | test accuracy: 0.76667\n",
      "Subset 2\n",
      "epoch:   1 | train accuracy: 0.10000 | test accuracy: 0.10000\n",
      "epoch:   2 | train accuracy: 0.28222 | test accuracy: 0.45556\n",
      "epoch:   3 | train accuracy: 0.53556 | test accuracy: 0.52222\n",
      "epoch:   4 | train accuracy: 0.55111 | test accuracy: 0.42222\n",
      "epoch:   5 | train accuracy: 0.56667 | test accuracy: 0.62222\n",
      "epoch:   6 | train accuracy: 0.70444 | test accuracy: 0.58889\n",
      "epoch:   7 | train accuracy: 0.74444 | test accuracy: 0.63333\n",
      "epoch:   8 | train accuracy: 0.79333 | test accuracy: 0.71111\n",
      "epoch:   9 | train accuracy: 0.83111 | test accuracy: 0.68889\n",
      "epoch:  10 | train accuracy: 0.84000 | test accuracy: 0.75556\n"
     ]
    }
   ],
   "source": [
    "# start_train_end_node_process()\n",
    "start_train_end_node_process_print_some(number_of_samples, print_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "juvenile-finish",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 0.0705, -0.0664,  0.0337, -0.0174, -0.0052], grad_fn=<SliceBackward>)\n",
      "tensor([ 0.0696, -0.0750,  0.0337, -0.0167, -0.0024], grad_fn=<SliceBackward>)\n"
     ]
    }
   ],
   "source": [
    "## As you can see, wieghts of local models are updated after training process\n",
    "print(main_model.fc2.weight[0,0:5])\n",
    "print(model_dict[\"model1\"].fc2.weight[0,0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enabling-granny",
   "metadata": {},
   "source": [
    "Let's compare the performance of federated main model, individual local models and centralized model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overhead-mystery",
   "metadata": {},
   "source": [
    "Federated main model vs individual local models before 1st iteration (on distributed test set)\n",
    "Since main model is randomly initialized and no action taken on it yet, its performance is very poor. Please before_acc_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "sexual-thing",
   "metadata": {},
   "outputs": [],
   "source": [
    "before_acc_table=compare_local_and_merged_model_performance(number_of_samples=number_of_samples)\n",
    "before_test_loss, before_test_accuracy = validation(main_model, test_dl, main_criterion)\n",
    "\n",
    "main_model= set_averaged_weights_as_main_model_weights_and_update_main_model(main_model,model_dict, number_of_samples) \n",
    "\n",
    "after_acc_table=compare_local_and_merged_model_performance(number_of_samples=number_of_samples)\n",
    "after_test_loss, after_test_accuracy = validation(main_model, test_dl, main_criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "desirable-sending",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Federated main model vs individual local models before FedAvg first iteration\n"
     ]
    },
    {
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       "        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>sample</th>\n",
       "      <th>local_ind_model</th>\n",
       "      <th>merged_main_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample 0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample 1</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample 2</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample 3</td>\n",
       "      <td>0.677778</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample 4</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample  local_ind_model  merged_main_model\n",
       "0  sample 0         0.800000                0.1\n",
       "1  sample 1         0.766667                0.1\n",
       "2  sample 2         0.755556                0.1\n",
       "3  sample 3         0.677778                0.1\n",
       "4  sample 4         0.777778                0.1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Federated main model vs individual local models before FedAvg first iteration\")\n",
    "before_acc_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "maritime-effectiveness",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Federated main model vs individual local models after FedAvg first iteration\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>sample</th>\n",
       "      <th>local_ind_model</th>\n",
       "      <th>merged_main_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample 0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.911111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample 1</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.755556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample 2</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.788889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample 3</td>\n",
       "      <td>0.677778</td>\n",
       "      <td>0.844444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample 4</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.844444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample  local_ind_model  merged_main_model\n",
       "0  sample 0         0.800000           0.911111\n",
       "1  sample 1         0.766667           0.755556\n",
       "2  sample 2         0.755556           0.788889\n",
       "3  sample 3         0.677778           0.844444\n",
       "4  sample 4         0.777778           0.844444"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Federated main model vs individual local models after FedAvg first iteration\")\n",
    "after_acc_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-library",
   "metadata": {},
   "source": [
    "# Federated main model vs centralized model before 1st iteration (on all test data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-hampton",
   "metadata": {},
   "source": [
    "Please be aware that the centralized model gets approximately %98 accuracy on all test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bottom-singles",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before 1st iteration main model accuracy on all test data:  0.1006\n",
      "After 1st iteration main model accuracy on all test data:  0.8421\n",
      "Centralized model accuracy on all test data:  0.9788\n"
     ]
    }
   ],
   "source": [
    "print(\"Before 1st iteration main model accuracy on all test data: {:7.4f}\".format(before_test_accuracy))\n",
    "print(\"After 1st iteration main model accuracy on all test data: {:7.4f}\".format(after_test_accuracy))\n",
    "print(\"Centralized model accuracy on all test data: {:7.4f}\".format(central_test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indoor-berlin",
   "metadata": {},
   "source": [
    "This is a single iteration, we can send the weights of the main model back to the nodes and repeat the above steps. Now let's check how the performance of the main model improves when we repeat the iteration 10 more times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aware-alfred",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2 : main_model accuracy on all test data:  0.8937\n",
      "Iteration 3 : main_model accuracy on all test data:  0.9070\n",
      "Iteration 4 : main_model accuracy on all test data:  0.9149\n",
      "Iteration 5 : main_model accuracy on all test data:  0.9221\n",
      "Iteration 6 : main_model accuracy on all test data:  0.9275\n",
      "Iteration 7 : main_model accuracy on all test data:  0.9322\n",
      "Iteration 8 : main_model accuracy on all test data:  0.9352\n",
      "Iteration 9 : main_model accuracy on all test data:  0.9390\n",
      "Iteration 10 : main_model accuracy on all test data:  0.9409\n",
      "Iteration 11 : main_model accuracy on all test data:  0.9439\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    model_dict=send_main_model_to_nodes_and_update_model_dict(main_model, model_dict, number_of_samples)\n",
    "    start_train_end_node_process_without_print(number_of_samples)\n",
    "    main_model= set_averaged_weights_as_main_model_weights_and_update_main_model(main_model,model_dict, number_of_samples) \n",
    "    test_loss, test_accuracy = validation(main_model, test_dl, main_criterion)\n",
    "    print(\"Iteration\", str(i+2), \": main_model accuracy on all test data: {:7.4f}\".format(test_accuracy))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cubic-desktop",
   "metadata": {},
   "source": [
    "The accuracy of the centralized model was calculated as approximately 98%. The accuracy of the main model obtained by FedAvg method started from 89% and improved to 94%. In this case, we can say that although the main model obtained by FedAvg method was trained without seeing the data, its performance cannot be underestimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "elder-liberia",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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