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 learning (FL) has been proposed as an emerging paradigm to perform privacy-
preserving distributed machine learning in the Internet of Things (IoT). However, the …

Abstract Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in
which wireless client-devices independently train their ML models and send the locally …

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications
such as UAV taxis and delivery drones. However, the limited battery energy restricts the …

Federated edge learning (FEEL) is a promising collaborative paradigm, which employs
edge devices (EDs) to train machine learning models for a federation. It opens countless …

We demonstrate that merely analog transmissions and match filtering can realize the
function of an edge server in federated learning (FL). Therefore, a network with massively …

Rapid technological advancements have resulted in more smart devices with an increased
volume of data in Industrial Internet of Things (IIoT) systems. These data often contain …

Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have
emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data …

While a practical wireless network has many tiers where end users do not directly
communicate with the central server, the users' devices have limited computation and …