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

To mitigate the multi-access latency in federated edge learning, an efficient broadband
analog transmission scheme has been recently proposed, featuring the aggregation of …

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 …

This article presents the first orthogonal frequency-division multiplexing (OFDM)-based
digital over-the-air computation (AirComp) system for wireless federated edge learning …

Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation
across multiple devices in federated edge learning (FEEL). In addition to the analog …

Over-the-air computation (AirComp) is an emerging wireless technique with wide
applications (eg, in distributed edge learning), which can swiftly compute functions of …

Federated learning (FL) is a framework for distributed learning of centralized models. In FL,
a set of edge devices train a model using their local data, while repeatedly exchanging their …

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile
users (MUs) aim to reach a consensus on a global model with the help of a parameter server …

The sixth-generation (6G) wireless communication is expected to support ubiquitous artificial
intelligence (AI) applications from the network core to the end devices. The computational …

In this paper, we study a federated learning system at the wireless edge that uses over-the-
air computation (Air-Comp). In such a system, users transmit their messages over a multi …