  Part I of this paper formulated a multitask optimization problem where agents
in the network have individual objectives to meet, or individual parameter
vectors to estimate, subject to a smoothness condition over the graph. A
diffusion strategy was devised that responds to streaming data and employs
stochastic approximations in place of actual gradient vectors, which are
generally unavailable. The approach relied on minimizing a global cost
consisting of the aggregate sum of individual costs regularized by a term that
promotes smoothness. We examined the first-order, the second-order, and the
fourth-order stability of the multitask learning algorithm. The results
identified conditions on the step-size parameter, regularization strength, and
data characteristics in order to ensure stability. This Part II examines
steady-state performance of the strategy. The results reveal explicitly the
influence of the network topology and the regularization strength on the
network performance and provide insights into the design of effective multitask
strategies for distributed inference over networks.
