Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated
remarkable success in a wide range of applications, driven by their ability to leverage vast …

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial.
By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic …

As healthcare data becomes increasingly available from various sources, including clinical
institutions, patients, insurance companies, and pharmaceutical industries, machine …

Federated learning is a promising approach in the Internet of Things (IoT) that enables
collaborative and distributed machine learning among massive IoT devices without sharing …

Abstract Unmanned Aerial Vehicles (UAVs) have become an integral part of modern smart
cities and systems. However, the proliferation of UAVs has also brought a significant security …

Smart healthcare has become a crucial area of research due to its potential to enhance
healthcare delivery, particularly in personalized healthcare. In this study, we conducted a …

Abstract Decentralized Federated Learning (DFL) is a prevalent approach to efficiently train
deep learning models and preserve privacy by sharing model gradients instead of the local …

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving
distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL …

Federated Learning (FL) has become a popular distributed machine learning technique that
preserves privacy of data set generated by the Internet of Things (IoT) devices. However …

In this paper, we present a novel blockchain-enabled approach to opportunistic federated
learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates …