Digital twin has been emerging as a promising paradigm that connects physical entities and
digital space, and continuously evolves to optimize the physical systems. In this article, we …

Federated learning (FL), as a potential machine learning framework for privacy preservation,
has gained significant attention. However, the considerable communication overhead …

In recent years, long-short term memory (LSTM) networks have emerged as a promising tool
for time-series analysis, offering the ability to capture temporal dependencies effectively …

Abstract Knowledge graphs (KGs) serve as invaluable tools for organizing and representing
structural information, enabling powerful data analysis and retrieval. In this paper, we …

This paper introduces a novel approach to personal risk (PR) identification using federated
learning (FL) in wireless communication scenarios, leveraging generalized information. The …

This paper introduces a novel approach utilizing normalized flow networks (NFNs) for
dynamic personal risk (PR) analysis, specifically focusing on the assessment of two-way …

A major bottleneck in distributed learning is the communication overhead of exchanging
intermediate model update parameters between the worker nodes and the parameter …

In recent years, big AI models have demonstrated remarkable performance in various
artificial intelligence (AI) tasks. However, their widespread use has introduced significant …

This study delves into the realm of multi-relay assisted Internet of Things (IoT) networks
within the context of mixed fading environments. Here, data transmission from the source to …

This study delves into Internet of Things (IoT) networks wherein a transmitting source
communicates information to a designated recipient. The presence of signal attenuation …