Federated learning is the process of develo** machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …

In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …

The smart healthcare system has improved the patients quality of life (QoL), where the
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …

Medical healthcare centers are envisioned as a promising paradigm to handle the massive
volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI …

Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …

Objectives Medical artificial intelligence (AI) has recently attracted considerable attention.
However, training medical AI models is challenging due to privacy-protection regulations …

In the present decades, analysis of healthcare domain plays a significant role for research
purposes and it depends more on computer technology. The analysis of medical data is one …

Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …