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20242026
most citedDistributed Swarm Learning for Edge Internet of Things

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SP2026

FM-RME: Foundation Model Empowered Radio Map Estimation

Dong Yang, Yue Wang, Songyang Zhang +3

Traditional radio map estimation (RME) techniques fail to capture multi-dimensional and dynamic characteristics of complex spectrum environments. Recent data-driven methods achieve…

cs.DC2025

Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation

Zhuoyu Yao, Yue Wang, Songyang Zhang +3

Recent advances in distributed learning systems have introduced effective solutions for implementing collaborative artificial intelligence techniques in wireless communication netw…

cs.LG2025

Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data

Zhuoyu Yao, Yue Wang, Songyang Zhang +3

Recent advances in distributed swarm learning (DSL) offer a promising paradigm for edge Internet of Things. Such advancements enhance data privacy, communication efficiency, energy…

eess.SP2025

Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications

Yueling Zhou, Achintha Wijesinghe, Yue Wang +2

Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To e…

cs.LG2025

Physics-Inspired Distributed Radio Map Estimation

Dong Yang, Yue Wang, Songyang Zhang +2

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). Whi…

cs.CR2024

Security Risks Concerns of Generative AI in the IoT

Honghui Xu, Yingshu Li, Olusesi Balogun +3

In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in…

cs.NI2024★ 1 cited

Distributed Swarm Learning for Edge Internet of Things

Yue Wang, Zhi Tian, FXin Fan +3

The rapid growth of Internet of Things (IoT) has led to the widespread deployment of smart IoT devices at wireless edge for collaborative machine learning tasks, ushering in a new…