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 …

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

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 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 …

Over-the-air federated learning (FL) is a recent paradigm to address the communication
bottleneck of FL, where a machine learning model is trained by aggregating the local …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …

Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

Federated learning (FL) is a promising technology which trains a machine learning model
on edge devices in a distributed manner orchestrated by a parameter server (PS). To realize …

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 …

Federated learning (FL) has recently emerged as a promising technology to enable artificial
intelligence (AI) at the network edge. To improve the communication efficiency of FL, over …