A multitask diffusion strategy with optimized inter-cluster cooperation

• Main Authors: Wang, Yuan, Tay, Wee Peng, Hu, Wuhua
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Distributed Estimation; Diffusion Strategy; DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/89364; http://hdl.handle.net/10220/47840
• Institution: Nanyang Technological University

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.