Abstract The Internet of Things (IoT) has embedded itself in our daily lives, offering smart
services and AI-driven applications. However, traditional AI methods face challenges due to …

The amount of data generated owing to the rapid development of the Smart Internet of
Things is increasing exponentially. Traditional machine learning can no longer meet the …

Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities
within digital communications, embedded hardware, and software. Arguably, the simplest …

Federated learning (FL), as a safe distributed training mode, provides strong support for the
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …

Federated Learning (FL), as a distributed machine learning paradigm, facilitates
collaborative training without sharing raw data and holds promise for effective application in …

Deep learning is increasingly utilized in data-driven tasks in Vehicular Ad-hoc Networks
(VANETs) such as traffic sign recognition or pedestrian detection, and is expected to fulfill …

Federated Learning is a decentralized machine learning technique that creates a global
model by aggregating local models from multiple edge devices without a need to access the …

Federated learning (FL), as a distributed machine learning paradigm, essentially promises
that multiple parties can jointly train the model collaboratively without sharing local data …

Integrating artificial intelligence (AI) and federated learning (FL) in smart transportation has
raised critical issues regarding their responsible use. Ensuring responsible AI is paramount …