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 …

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

On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest owing to their superior privacy, reduced latency, and bandwidth …

In the context of mobile edge computing (MEC), it can be quite challenging to provide and
allocate multiple resources from heterogeneous MEC servers for remote execution of tasks …

In 5G and beyond, newly emerging services, such as edge computing/intelligence services,
may demand the provisioning of heterogeneous communications, computing, and storage …

The vehicular networks are envisioned to support the future intelligent transportation system
(ITS). Content dissemination is vital to achieving efficient data traffic management and real …

In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-
time data processing and analysis by offloading to the MEC servers. The objective is …

As mobile applications grow increasingly computation-intensive, the challenges arising from
the limitations of mobile devices in terms of computing resources and battery life become …

Multi-access edge computing (MEC) is a promising technology to enhance the quality of
service, particularly for low-latency services, by enabling computing offloading to edge …