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...