  Recent research works on distributed adaptive networks have intensively
studied the case where the nodes estimate a common parameter vector
collaboratively. However, there are many applications that are
multitask-oriented in the sense that there are multiple parameter 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
$\ell_2$-regularization. Some results on the mean-square stability and
convergence of the algorithm are also provided. Simulations are conducted to
illustrate the theoretical findings.
