Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …

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

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

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

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 …

To enable communication-efficient federated learning, fast model aggregation can be
designed using over-the-air computation (AirComp). In order to implement a reliable and …

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 …

In this paper, a communication-efficient federated learning (FL) framework is proposed for
improving the convergence rate of FL under a limited uplink capacity. The central idea of the …

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 …