We propose an autonomous blockchain-based federated learning (BFL) design for privacy-
aware and efficient vehicular communication networking, where local on-vehicle machine …

In this paper, we propose an autonomous blockchain-based federated learning (BFL)
design for privacy-aware and efficient vehicular communication networking, where local on …

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart
decisions, improving vehicular services and enhancing the driving experience through a …

The accuracy of the ML model is essential for the further development of AI-enabled CAVs.
With the increasing complexity of on-board sensor systems, the large amount of raw data …

The advances in today's IoT devices and machine learning methods have given rise to the
concept of Federated Learning. Through such a technique, a plethora of network devices …

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in
a collaborative manner. Previous works focus on data sharing between vehicles, but a large …

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can
improve the driving experience and service quality. However, the bandwidth, security and …

The emerging advances in personal devices and privacy concerns have given the rise to the
concept of Federated Learning. Federated Learning proves its effectiveness and privacy …

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 …

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL)
architecture where local learning model updates are exchanged and verified. This enables …