On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Recently, in-car monitoring has emerged as a promising technology for detecting early-
stage abnormal status of the driver and providing timely alerts to prevent traffic accidents …

The increasing complexity of deep neural networks poses significant barriers to
democratizing them to resource-limited edge devices. To address this challenge, split …

Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic
congestion, accidents, and severe carbon emissions. In order to address this essential issue …

Collaborative perception has recently gained significant attention in autonomous driving,
improving perception quality by enabling the exchange of additional information among …

Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …