Federated edge learning is envisioned as the bedrock of enabling intelligence in next-
generation wireless networks, but the limited spectral resources often constrain its …

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 …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

As one of the most popular and attractive frameworks for model training, federated edge
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …

Federated edge learning is a promising technology to deploy intelligence at the edge of
wireless networks in a privacy-preserving manner. Under such a setting, multiple clients …

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

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

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 …

Over-the-air computation (AirComp) is a promising technology converging communication
and computation over wireless networks, which can be particularly effective in model …

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 …