most citedConvergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

6 citations · 12 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CR2025

Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks

Xinye Cao, Yihan Lin, Guoshun Nan +9

Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability and adaptability required for th…

cs.NI2025

Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks

Zhuoran Duan, Yuhao Wei, Guoshun Nan +9

Large models (LMs), such as ChatGPT, have made a significant impact across diverse domains and hold great potential to facilitate the evolution of network intelligence. Wireless-na…

cs.NI2025

Agile Orchestration at Will: An Entire Smart Service-Based Security Architecture Towards 6G

Zhuoran Duan, Guoshun Nan, Rushan Li +8

The upcoming 6G will fundamentally reshape mobile networks beyond communications, unlocking a multitude of applications that were once considered unimaginable. Meanwhile, security…

cs.LG20256 cited

Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

Xiyu Zhao, Qimei Cui, Weicai Li +5

Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide persona…

cs.DC20252 cited

A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams

Xiyu Zhao, Qimei Cui, Wei Ni +5

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can re…

cs.DC20254 cited

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling

Xiyu Zhao, Qimei Cui, Ziqiang Du +6

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). L…