Combined optimization of Quality of Service (QoS) and security/privacy has always been an
overlooked topic among IT professionals. The main reason for this is the difficulty to …

Abstract Internet of Medical Things (IoMT) is a system in which different types of devices
actively integrate with other physical and virtual resources accessible on the internet, thus …

The process of data aggregation, which involves the collection and consolidation of data
from diverse sources into a unified dataset, is of crucial significance in the analysis of trends …

Federated learning (FL), a cooperative distributed learning framework, has been employed
in various intelligent Internet of Things (IoT) applications (eg, smart health-care, smart home …

The ever-expanding landscape of the Internet of Medical Things (IoMT) is increasingly
reliant on Federated Learning (FL) to construct cooperative, privacy-centric AI models. By …

Federated Learning represents a revolutionary approach in artificial intelligence,
emphasizing decentralized and privacy-preserving methodologies. Unlike traditional …

As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices
have permeated daily life. Service providers are actively collecting copious numbers of user …

The field of smart healthcare has witnessed a remarkable revolution propelled by recent
advancements in communication technologies and complemented by pivotal support …

Federated learning preserves the privacy of user data through Machine Learning (ML). It
enables the training of an ML model during this process. The Healthcare Internet of Things …

Suppliers face significant challenges when discussing cloud-based health apps, such as
implementing highly secure procedures and rigorous privacy safeguards. This applies to the …