  In the past few decades, machine learning has revolutionized data processing
for large scale applications. Simultaneously, increasing privacy threats in
trending applications led to the redesign of classical data training models. In
particular, classical machine learning involves centralized data training,
where the data is gathered, and the entire training process executes at the
central server. Despite significant convergence, this training involves several
privacy threats on participants' data when shared with the central cloud
server. To this end, federated learning has achieved significant importance
over distributed data training. In particular, the federated learning allows
participants to collaboratively train the local models on local data without
revealing their sensitive information to the central cloud server. In this
paper, we perform a convergence comparison between classical machine learning
and federated learning on two publicly available datasets, namely,
logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset.
The simulation results demonstrate that federated learning achieves higher
convergence within limited communication rounds while maintaining participants'
anonymity. We hope that this research will show the benefits and help federated
learning to be implemented widely.
