Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

The latest 5G mobile networks have enabled many exciting Internet of Things (IoT)
applications that employ unmanned aerial vehicles (UAVs/drones). The success of most …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

The use of machine learning (ML) with electronic health records (EHR) is growing in
popularity as a means to extract knowledge that can improve the decision-making process in …