Federated learning (FL) enables edge devices to collaboratively learn a model in a
distributed fashion. Many existing researches have focused on improving communication …

Federated learning has shown its advances recently but is still facing many challenges, such
as how algorithms save communication resources and reduce computational costs, and …

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing,
computing, and communication capabilities have motivated intelligent applications …

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-
input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) …

Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

Conventional frequentist federated learning (FL) schemes are known to yield overconfident
decisions. Bayesian FL addresses this issue by allowing agents to process and exchange …

In cellular networks, resource allocation is performed in a centralized way, which brings
huge computation complexity to the base station (BS) and high transmission overhead. This …

Real-time License Plate Recognition plays a significant role in traffic congestion control and
road safety monitoring. Practically, a network camera may capture multiple license plates in …

Federated learning (FL) as a new decentralized learning/computing technique has potential
advantages (eg, accelerating computation task processing and protecting user privacy) for …