  Machine learning models based on sensitive data in the real-world promise
advances in areas ranging from medical screening to disease outbreaks,
agriculture, industry, defense science, and more. In many applications,
learning participant communication rounds benefit from collecting their own
private data sets, teaching detailed machine learning models on the real data,
and sharing the benefits of using these models. Due to existing privacy and
security concerns, most people avoid sensitive data sharing for training.
Without each user demonstrating their local data to a central server, Federated
Learning allows various parties to train a machine learning algorithm on their
shared data jointly. This method of collective privacy learning results in the
expense of important communication during training. Most large-scale
machine-learning applications require decentralized learning based on data sets
generated on various devices and places. Such datasets represent an essential
obstacle to decentralized learning, as their diverse contexts contribute to
significant differences in the delivery of data across devices and locations.
Researchers have proposed several ways to achieve data privacy in Federated
Learning systems. However, there are still challenges with homogeneous local
data. This research approach is to select nodes (users) to share their data in
Federated Learning for independent data-based equilibrium to improve accuracy,
reduce training time, and increase convergence. Therefore, this research
presents a combined Deep-QReinforcement Learning Ensemble based on Spectral
Clustering called DQRE-SCnet to choose a subset of devices in each
communication round. Based on the results, it has been displayed that it is
possible to decrease the number of communication rounds needed in Federated
Learning.
