16 citations · 20 across the 3 of their papers we have counts for
4 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…
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…
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…
Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction
Xi Yu, Tiejun Lv, Weicai Li +3
Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extra…