In this study, we propose an over-the-air computation (OAC) scheme to calculate the
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …

The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …

We study distributed machine learning at the wireless edge, where limited power devices
(workers) with local datasets implement distributed stochastic gradient descent (DSGD) over …

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 demonstrate that merely analog transmissions and match filtering can realize the
function of an edge server in federated learning (FL). Therefore, a network with massively …

Federated learning (FL) is an emerging technique that enables privacy-preserving
distributed learning. Most related works focus on centralized FL, which leverages the …

This paper develops a class of low-complexity device scheduling algorithms for over-the-air
federated learning via the method of matching pursuit. The proposed scheme tracks closely …

In this paper, we consider a problem in which distributively extracted features are used for
performing inference in wireless networks. We elaborate on our proposed architecture …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

This paper presents NCOTA-DGD, a Decentralized Gradient Descent (DGD) algorithm that
combines local gradient descent with a novel Non-Coherent Over-The-Air (NCOTA) …