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

The scalability of large language models (LLMs) in handling high-complexity models and
large-scale datasets has led to tremendous successes in pivotal domains. While there is an …

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

Collaborative perception systems leverage multiple edge devices, such surveillance
cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite …

Autonomous driving has attracted significant attention from both academia and industries,
which is expected to offer a safer and more efficient driving system. However, current …

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 …

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 …

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration
stemming from data heterogeneity in federated learning (FL) by grouping similar clients for …

Collaborative perception (CP) leverages visual data from connected and autonomous
vehicles (CAV) to enhance an ego vehicle's field of view (FoV). Despite recent progress …

Connected and autonomous vehicles (CAVs) have garnered significant attention due to their
extended perception range and enhanced sensing coverage. To address challenges such …