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 …

The arrival of autonomous vehicles (AVs) promises many great benefits, including increased
safety and reduced energy consumption, pollution, and congestion. However, these engines …

Autonomous driving is an active research topic in both academia and industry. However,
most of the existing solutions focus on improving the accuracy by training learnable models …

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of
patient data. Since large and diverse datasets for training of Machine Learning (ML) models …

Medical data is often highly sensitive in terms of data privacy and security concerns.
Federated learning, one type of machine learning techniques, has been started to use for …

Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things
(IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by …

Classical and centralized Artificial Intelligence (AI) methods require moving data from
producers (sensors, machines) to energy hungry data centers, raising environmental …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …