In an emergency healthcare situation, delay between injury and treatment is one of the most
critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by …

Federated Learning (FL) enables local model training on devices while collaboratively
updating a global model on a server, ensuring user data privacy by keeping it on the device …

Abstract Federated Learning (FL) trains AI models in healthcare without sharing patient data.
FL computes client models locally and combines them to create a global model. However …

Service migration, determining when, where and how to migrate the ongoing service, is of
paramount importance in mobile edge computing (MEC) for provisioning high quality of …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

The significant heterogeneity of data resources in Internet of Medical Things (IoM T) devices
profoundly affects the efficacy of federated learning (FL) when training medical models …

In an emergency healthcare situation, the time delay between injury and treatment is crucial
for patient survival. Real-time processing of ambulance data can significantly reduce …

Mobile edge computing is gaining traction due to its ability to deliver ultra-low-latency
services for mobile applications. This is achieved through a federation of edge clouds in …

Mobile-edge computing (MEC) is a promising approach to support high-quality time-
sensitive applications. With the increasing number of mobile devices, achieving efficient …

Over the past decade, there has been a significant surge in consumer application services
and server connectivity, and this trend is expected to double in 2030. The primary …