Implementing federated averaging

In this section, we will implement federated averaging with a practical use case in Python. Note that while we are using the MNIST dataset here as an example, this can easily be replicated for any dataset of your choosing.

Importing libraries

We begin by importing the necessary libraries. We will need our standard Python libraries, along with some libraries from Keras, which will allow us to create our deep learning model. The following code snippet imports these libraries:

import numpy as np
import random
import cv2
from imutils import paths
import os
# SkLearn Libraries
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score
# TensorFlow Libraries
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow...