In this paper, we propose a framework where over-the-air computation (OAC) occurs in both
uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

In distributed edge networks employing frequency division duplex, the feedback of channel
state information (CSI) from the edge devices to the edge server always consumes a lot of …

In this work, we investigate the challenging problem of on-demand federated learning (FL)
over heterogeneous edge devices with diverse resource constraints. We propose a cost …

The rapid development of machine learning and wireless communication is creating a new
paradigm for future networks, namely edge-intelligent networks. Specifically, data generated …

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over
wireless networks has garnered increasing attention, which is called Federated Edge …

In this paper, we consider the problem of Federated Learning (FL) under non-iid data
setting. We provide an improved estimate of the empirical loss at each node by using a …

Federated edge learning (FEEL) is a promising distributed machine learning (ML)
framework to drive edge intelligence applications. However, due to the dynamic wireless …

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 …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …