Autonomous systems are becoming inherently ubiquitous with the advancements of
computing and communication solutions enabling low-latency offloading and real-time …

Blockchain and cloud-edge computing techniques are promising technologies for next-
generation, secure, and privacy-preserving data-sharing systems. By integrating these …

With smart sensors and embedded drivers, today's automotive industry has taken a giant
leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet …

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable
mechanism for decentralized transaction validation and is widely used in financial economy …

Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their
ability to reduce vehicle emissions. Developing an intelligent system to manage EVs …

As a promising distributed learning paradigm, federated learning (FL) involves training deep
neural network (DNN) models at the network edge while protecting the privacy of the edge …

Vehicular ad-hoc networks (VANETs) are increasingly commonplace, partly due to the
popularity of electric vehicles and the digitalization of cities. Data collected and shared in …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

Abstract Asynchronous Federated Learning (AFL) has been introduced to improve the
efficiency of FL by reducing the latency of Machine Learning (ML) model aggregation …

In the Social Internet of Vehicles (SIoV), federated learning is able to significantly protect the
private data of the vehicle's client, while reducing the transmission load between entities …