Tony Q. S. Quek

Interference Mitigation in LEO Satellite Communications: A Multi-Timescale Perspective

With the continued deployment of low Earth orbit (LEO) satellite constellations, spectrum resources are becoming more limited. As a result, satellite-to-ground links face many types of strongly coupled interference in complex electromagnetic environments. This interference includes non-intentional interference caused b...

Maximizing Accuracy-Per-Cost in Vehicular Federated Learning via Active Inference and Two-Stage Resource Optimization

Federated Learning (FL) enables collaborative model training in the Internet of Vehicles (IoV), helping vehicles overcome data and computational limitations while preserving privacy. However, its performance is constrained by both data heterogeneity and system heterogeneity. Additionally, different FL tasks exhibit div...

AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition

Asymmetric Partial Model Transmission for Federated Edge Learning

Federated learning (FL) applications normally employ large deep learning (DL) models, resulting in excessive communication overhead in the deployment of FL over resource-constraint mobile edge networks. To achieve better scalability for DL-based FL, we capitalize on both the asymmetric nature of mobile networks and the...