activity
20172024
most citedDeep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

539 citations · 1.3k across the 43 of their papers we have counts for

collaborators

55 papers

cs.CR2024

Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

Changyuan Zhao, Hongyang Du, Dusit Niyato +6

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of atten…

cs.AI20226 cited

When Quantum Information Technologies Meet Blockchain in Web 3.0

Minrui Xu, Xiaoxu Ren, Dusit Niyato +5

With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distribu…

eess.SP2022

Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications

Hongyang Du, Jiacheng Wang, Dusit Niyato +4

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted…

cs.NI202214 cited

EPViSA: Efficient Auction Design for Real-time Physical-Virtual Synchronization in the Metaverse

Minrui Xu, Dusit Niyato, Benjamin Wright +5

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no lo…

cs.NI2022

Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

Minrui Xu, Dusit Niyato, Jiawen Kang +2

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile use…

eess.SY2022

Multi-Resource Allocation for On-Device Distributed Federated Learning Systems

Yulan Gao, Ziqiang Ye, Han Yu +3

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL…