4 papers
HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning
Xiaohong Yang, Minghui Liwang, Xianbin Wang +4
The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient Machine Learning (ML) solutions that can handle high vehicular mobility and decentralized data. This h…
PAST: Pilot and Adaptive Orchestration for Timely and Resilient Service Delivery in Edge-Assisted UAV Networks under Spatio-Temporal Dynamics
Houyi Qi, Minghui Liwang, Liqun Fu +4
Incentive-driven resource trading is essential for uncrewed aerial vehicle (UAV) applications with intensive, time-sensitive computing demands. Traditional spot trading suffers fro…
Oh-Trust: Overbooking and Hybrid Trading-empowered Resource Scheduling with Smart Reputation Update over Dynamic Edge Networks
Houyi Qi, Minghui Liwang, Liqun Fu +3
Incentive-driven computing resource sharing is crucial for meeting the ever-growing demands of emerging mobile applications. Although conventional spot trading offers a solution, i…
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology
Minghong Wu, Minghui Liwang, Yuhan Su +5
Federated Learning (FL) offers a pioneering distributed learning paradigm that enables devices/clients to build a shared global model. This global model is obtained through frequen…