In this paper, we propose a framework where over-the-air computation (OAC) occurs in both
uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the …

In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV)(CSC-MV),
a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated …

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 …

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge
learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the …

In this study, we propose an over-the-air computation (OAC) scheme to calculate the
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …

Federated Edge Learning (FEEL) is a distributed machine learning technique where each
device contributes to training a global inference model by independently performing local …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …