We consider a multitask network where nodes 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 strategy, we propose a multitask diffusion strategy whose mean and mean-square stability can be achieved independent of the inter-cluster cooperation weights. We develop a distributed optimization algorithm that allows each node in the network to locally optimize its inter-cluster cooperation weights. Simulation results demonstrate that our approach leads to a lower average steady-state network MSD, compared with the multitask diffusion strategy using an averaging rule for the inter-cluster cooperation.
['Yuan Wang', 'Wee Peng Tay', 'Wuhua Hu']
Multitask diffusion LMS with optimized inter-cluster cooperation