Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …

We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …

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 paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the
superposition property of the wireless medium, and performs model aggregation over the air …

We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

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 …

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

Over-the-air computation (AirComp) has recently been identified as a prominent technique
to enhance communication efficiency of wireless federated learning (FL). This letter …

Motivated by the increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …