We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient federated learning at the wireless network edge, with latency and learning …

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets train a joint model with the help of a remote parameter server (PS). We …

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 …

In this paper, we propose a communication-efficient federated learning (FL) framework to
enhance the convergence rate of FL under limited uplink capacity. The core idea of our …

This paper proposes an over-the-air aggregation framework for federated learning (FL) in
broadband wireless networks where not only edge devices but also a base station (BS) has …

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 …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

Under the organization of the base station (BS), wireless federated learning (FL) enables
collaborative model training among multiple devices. However, the BS is merely responsible …