In this paper, we consider a beamforming vector design and device selection problem in
over-the-air computation (AirComp) for federated learning. Since the learning performance …

Introducing adaptiveness to federated learning has recently ushered in a new way to
optimize its convergence performance. However, adaptive learning strategies originally …

We focus on a distributed learning problem in a communication network, consisting of
distributed nodes and a central parameter server (PS). The PS is responsible for performing …

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 …

With the advent of the Internet of things (IoT) era, federated learning plays an important role
in breaking through traditional data barriers and effectively realizing data privacy and …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …

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 …

Over-the-air computing (AirComp) has recently attracted considerable attention as an
efficient method of data fusion by integrating uncoded communication transmissions with …

We propose a novel communication design, termed random orthogonalization, for federated
learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The …

Distributed optimization is ubiquitous in emerging applications, such as robust sensor
network control, smart grid management, machine learning, resource slicing, and …