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In multi-agent networked systems, parameter estimation problems arising in many practical applications are often required to solve Non-Linear Least Squares (NLLS) problems with the usual objective function (i.e., sum of squared residuals). The aim is to estimate a global parameter of interest across the network, such that the discrepancy between the estimation model and the real output of the system is minimized. There are challenges to face when applying the conventional Gauss-Newton method, such as non-cooperation and prosaic learning behavior. In this paper, we propose two Gauss-Newton type fusion estimation algorithms for solving overdetermined NLLS optimization problems arising frequently in multi-agent networked environment. One is the cycle-based Gauss-Newton (CGN) algorithm that is more attractive in performance due to its distributed nature than its peer: the known centralized Gauss …