5 papers
Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network
Bibo Wu, Fang Fang, Ming Zeng +1
Leveraging pinching antennas in wireless network enabled federated learning (FL) can effectively mitigate the common "straggler" issue in FL by dynamically establishing strong line…
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems
Bibo Wu, Fang Fang, Xianbin Wang
Federated Learning (FL) has gained significant attention in recent years due to its distributed nature and privacy preserving benefits. However, a key limitation of conventional FL…
Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks
Bibo Wu, Fang Fang, Xianbin Wang
Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computing resources of distributed clients and t…
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning
Bibo Wu, Fang Fang, Xianbin Wang +3
Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still re…
Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks
Bibo Wu, Fang Fang, Xianbin Wang
In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally. Nevertheless, the performance of FL…