most citedSemi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis

6 citations · 6 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2025★ 6 cited

Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis

Yajiao Dai, Jun Li, Zhen Mei +5

Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised d…

eess.SP2025

Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systems

Yiyang Ni, Qi Zhang, Guangji Chen +3

With the development of the upcoming sixth-generation networks (6G), reconfigurable intelligent surfaces (RISs) have gained significant attention due to its ability of reconfigurin…

eess.SP2025

Attention-Enhanced Prompt Decision Transformers for UAV-Assisted Communications with AoI

Chi Lu, Yiyang Ni, Zhe Wang +3

Decision Transformer (DT) has recently demonstrated strong generalizability in dynamic resource allocation within unmanned aerial vehicle (UAV) networks, compared to conventional d…

eess.SP2025

Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition

Jie Zhang, Yiyang Ni, Jun Li +6

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuousl…

eess.SP2024

IRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff

Guangji Chen, Jun Li, Qingqing Wu +2

This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train…

cs.LG2024

Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT

Jixuan Cui, Jun Li, Zhen Mei +3

The challenge of data scarcity hinders the application of deep learning in industrial surface defect classification (SDC), as it's difficult to collect and centralize sufficient tr…