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.
Cite
@article{arxiv.2602.06070,
title = {Computationally Efficient Laplacian CL-colME},
author = {Nikola Stankovic},
journal= {arXiv preprint arXiv:2602.06070},
year = {2026}
}