10 papers
Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication
Lin Yin, Tiejun Lv, Weicai Li +2
Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). However, pushing topology-agnostic aggregati…
Adaptive Dual-Path Framework for Covert Semantic Communication
Xi Yu, Weicai Li, Lin Yin +1
This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission with task-oriented semantic co…
Joint Routing and Model Pruning for Decentralized Federated Learning in Bandwidth-Constrained Multi-Hop Wireless Networks
Xiaoyu He, Weicai Li, Tiejun Lv +1
Decentralized federated learning (D-FL) enables privacy-preserving training without a central server, but multi-hop model exchanges and aggregation are often bottlenecked by commun…
Navigating the Dual-Use Nature and Security Implications of Reconfigurable Intelligent Surfaces in Next-Generation Wireless Systems
Hetong Wang, Tiejun Lv, Yashuai Cao +4
Reconfigurable intelligent surface (RIS) technology offers significant promise in enhancing wireless communication systems, but its dual-use potential also introduces substantial s…
Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?
Weicai Li, Tiejun Lv, Xiyu Zhao +2
Inherent communication noises have the potential to preserve privacy for wireless federated learning (WFL) but have been overlooked in digital communication systems predominantly u…
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…