With the proliferation of connected devices including smartphones, novel network
connectivity and management methods are needed to meet user Quality of Experience …

This paper proposes a novel localization algorithm using the reconfigurable intelligent
surface (RIS) received signal, ie, RIS information. Compared with BS received signal, ie, BS …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

Within the realm of Mobile Edge Computing (MEC), task offloading has consistently
garnered significant attention. Within the context of intricate caching environments and multi …

With the rapid growth of the Industrial Internet of Things, a large amount of industrial data
that needs to be processed promptly. Edge computing-based computation offloading can …

At the network edges, the multi-tier computing framework provides mobile users with efficient
cloud-like computing and signal processing capabilities. Deploying digital twins in the multi …

Most existing wireless federated learning (FL) studies focused on homogeneous model
settings where devices train identical local models. In this setting, the devices with poor …

The conventional model aggregation-based federated learning (FL) approach requires all
local models to have the same architecture, which fails to support practical scenarios with …

The computing in the network (COIN) paradigm is a promising solution that leverages
unused network resources to perform tasks to meet computation-demanding applications …

Existing device scheduling works in wireless federated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and require all devices to …