Federated Learning with Correlated Data
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller’s available information, measured by […]
Distributed Learning in Wireless Networks
The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected […]
Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning
The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients’ local computation capabilities. […]
Intelligent Radio Signal Processing
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network […]
Bayesian Inference Federated Learning for Heart Rate Prediction
The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable […]
Federated Machine Learning
The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be […]
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory
This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by […]
Content Popularity Prediction in Fog Radio Access Networks
In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction […]
Federated Learning under Channel Uncertainty
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy […]
L-FGADMM
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worker […]