Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation

TL;DR

This paper introduces an alternating Gaussian belief propagation (AGBP) algorithm for distributed maximum likelihood estimation in linear models, demonstrating improved convergence and scalability for large-scale IoT networks through analytical and numerical validation.

Contribution

The paper proposes a novel AGBP algorithm that alternates between inter- and intra-cluster iterations, enhancing convergence in distributed linear model inference across edge computing nodes.

Findings

AGBP outperforms existing methods in convergence speed.

The algorithm is scalable and effective for large-scale IoT networks.

Analytical and numerical results validate the approach.

Abstract

We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive internet of things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intra-cluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyse AGBP algorithm across a wide range of linear models characterised by symmetric and non-symmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.