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 …

Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

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

The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

The rapid advancement of artificial intelligence technologies has given rise to diversified
intelligent services, which place unprecedented demands on massive connectivity and …

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many
edge devices to collaboratively train a global model without sharing their private data. To …