Computationally Efficient Laplacian CL-colME

TL;DR

This paper introduces CL-colME, a Laplacian-based consensus method for decentralized mean estimation that improves computational efficiency while maintaining accuracy and convergence properties.

Contribution

The paper proposes CL-colME, a novel Laplacian-based consensus algorithm for colME that reduces computational complexity compared to existing methods.

Findings

CL-colME maintains convergence and accuracy of C-colME.

CL-colME offers improved computational efficiency.

Simulation results validate the effectiveness of CL-colME.

Abstract

Decentralized collaborative mean estimation (colME) is a fundamental task in heterogeneous networks. Its graph-based variants B-colME and C-colME achieve high scalability of the problem. This paper evaluates the consensus-based C-colME framework, which relies on doubly stochastic averaging matrices to ensure convergence to the oracle solution. We propose CL-colME, a novel variant utilizing Laplacian-based consensus to avoid the computationally expensive normalization processes. Simulation results show that the proposed CL-colME maintains the convergence behavior and accuracy of C-colME while improving computational efficiency.