In this paper, we investigate over-the-air model aggregation in a federated edge learning
(FEEL) system. We introduce a Markovian probability model to characterize the intrinsic …

Over-the-air computation (AirComp) has emerged as a new analog power-domain non-
orthogonal multiple access (NOMA) technique for low-latency model/gradient-updates …

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

Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …

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

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) is a communication-efficient framework for
distributed machine learning using training data distributed at edge devices. This framework …

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great
attention due to the outstanding performance in improving communication efficiency for …

Federated learning (FL) is a machine learning framework, where multiple distributed edge
Internet of Things (IoT) devices collaboratively train a model under the orchestration of a …