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.

翻译：去中心化协同均值估计（colME）是异构网络中的一项基础任务。其基于图的变体B-colME和C-colME实现了该问题的高可扩展性。本文评估了基于共识的C-colME框架，该框架依赖双随机平均矩阵来确保收敛到最优解。我们提出了CL-colME，这是一种利用基于拉普拉斯的共识来避免计算成本高昂的归一化过程的新颖变体。仿真结果表明，所提出的CL-colME在保持C-colME收敛行为和精度的同时，提高了计算效率。