Diffusion LMS for Clustered Multitask Networks

Recent research works on distributed adaptive networks have inten- sively studied the case where the nodes estimate a common parame- ter vector collaboratively. However, there are many applications that are multitask-oriented in the sense that there are multiple parame- ter vectors that need to be inferred simultaneously. In this paper, we employ diffusion strategies to develop distributed algorithms that address clustered multitask problems by minimizing an appropriate mean-square error criterion with regularization. Some results on the mean-square stability and convergence of the algorithm are also provided. Simulations are conducted to illustrate the theoretical findings.