In this study, we propose a digital over-the-air computation (OAC) scheme for achieving
continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

When implementing hierarchical federated learning over wireless networks, scalability
assurance and the ability to handle both interference and device data heterogeneity are …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

Federated learning (FL) has been recognized as a promising distributed learning paradigm
to support intelligent applications at the wireless edge, where a global model is trained …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and learning …

Over-the-air federated learning (OTA-FL) integrates communication and model aggregation
by exploiting the innate superposition property of wireless channels. The approach renders …

We propose an improved convergence analysis technique that characterizes the distributed
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …

To leverage rich data distributed at the network edge, a new machine-learning paradigm,
called edge learning, has emerged where learning algorithms are deployed at the edge for …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …