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This paper addresses a system identification problem for multiple isolated systems whose parameters are partially identical. An approach to the problem is a centralized identification; a single estimator collects all the input-output data from all of the systems to estimate their parameters at once. However, the approach can be practically infeasible when it is applied to a large number of systems due to the limitation of computational and communication resources. To solve the issue, we propose a method of a networked identification composed of two stages: 1) Multiple estimators collect the data from their own target systems to independently derive temporary estimates of their parameters and a covariance matrix of the estimates. 2) Then, with communicating the temporary estimates and the covariance matrix, they update their estimates. We also show the optimality of the proposed networked identification method with respect to the modeling accuracy, which is equivalently achieved by the centralized identification. Finally, we show the effectiveness of the proposed method in a numerical simulation.