However, to the best of our knowledge, there is no present work that simultaneously addresses all of these aspects. For example, in [@mota2015distributed], the setup involves a static network connected at all times with each agent having access to an incremental first order oracle, i.e., access to exact gradient information; the paper establishes convergence the iterate sequences to the optimizer, however, rates of convergence are not provided. In [@alghunaim2017distributed], the authors consider coupled distributed stochastic optimization setups where the coupling is induced by interest sets of different agents over static networks. The setup in [@alghunaim2017distributed] encompasses estimation setups, given that *global observability*[^4] holds for each entry of the parameter in the respective clusters, which in turn is subsumed in the setup. Technically speaking, typical distributed optimization setups rely on *local observability*[^5] without assuming *local correctness*[^6] at each agent. However, in the case of distributed estimation, the agents lack *local observability* but preserve *local correctness*. Moreover, the study of the mean square error in [@alghunaim2017distributed] reflects errors in terms of the step sizes only and does not reflect explicit dependence in terms of the number of agents collaborating to estimate a particular entry of the parameter. In comparison with [@mota2015distributed; @alghunaim2017distributed], 