In this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …

In this paper, the problem of delay minimization for federated learning (FL) over wireless
communication networks is investigated. In the considered model, each user exploits limited …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

In this article, the deployment of federated learning (FL) is investigated in an energy
harvesting wireless network in which the base stations (BSs) employs massive multiple …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, a wireless network is considered in which …

Federated learning is a distributed machine learning technology that can protect users' data
privacy, so it has attracted more and more attention in the industry and academia …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

This paper investigates a wireless federated learning (FL) network with limited
communication bandwidth, where multiple mobile clients train their individual models with …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …