Sending data to the cloud for analysis was a prominent trend during the past decades,
driving cloud computing as a dominant computing paradigm. However, the dramatically …

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 …

Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-
delay computation requirements for resource-constrained Internet of things (IoT) devices …

Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet
connectivity to meet the requirements of different applications, ranging from virtual reality to …

Advances in communications and networking technologies are driving the computing
paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data …

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

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

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-
hungry and latency-sensitive services by enabling the placement of services and execution …

Wireless mesh networks (WMNs) are a kind of wireless network technology that can transmit
multi-hop information, and have been regarded as one of the key technologies for …

Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …