Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

The development of applications based on artificial intelligence and implemented over
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …

Sudden medical safety accidents or large-scale outbreaks of epidemics will lead to a surge
in traffic near hospitals and other medical infrastructure, and traditional edge base stations …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

In the face of increasing concerns around privacy and security in the use of unmanned aerial
vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to …

This paper considers an unmanned aerial vehicle (UAV)-assisted Hierarchical Federated
Learning (HFL), where UAVs act as intermediate aggregators. We formulate an optimization …

The unique characteristics of Unmanned Aerial Vehicle (UAV) networks, including limited
resources, dynamic topology, and heterogeneity, pose significant challenges for integration …

Deploying federated learning (FL) applications in unmanned aerial vehicle (UAV)-assisted
wireless networks can enable ground terminals (GTs) to perform complex machine learning …

This paper investigates a learning-based approach autonomously and jointly optimizing the
trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent …