Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model

Xiang Li1; Weijie Yuan2; Kecheng Zhang1; Nan Wu3

2024

10.1109/JIOT.2024.3432114

IEEE Internet of Things Journal   影响因子和分区

2372-2541

To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using edge learning methods, several approaches have been proposed combining message passing (MP) algorithms. In this paper, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.

EI

第一

1.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China
2.School of System Design and Intelligent Manufacturing and the Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China
3.School of Information and Electronics, Beijing Institute of Technology, Beijing, China

Xiang Li,Weijie Yuan,Kecheng Zhang,et al. Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model[J]. IEEE Internet of Things Journal,2024,PP(99).

Xiang Li,Weijie Yuan,Kecheng Zhang,&Nan Wu.(2024).Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model.IEEE Internet of Things Journal,PP(99).

Xiang Li,et al."Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model".IEEE Internet of Things Journal PP.99(2024).