Nonlinear system identification with model structure selection via distributed computation

The problem of identifying a model of a system from input/output observations is typically formulated as an optimization problem over all available data that are collected by a central unit, in the same operating conditions. However, the massive diffusion of networked systems is changing this paradigm: data are collected separately by multiple agents and cannot be made available to some central unit due to, e.g., privacy constraints. In this paper, we address this novel set-up and consider the case in which multiple agents are cooperatively aiming at identifying a model for a nonlinear system, by performing local computations on their private data sets. The problem of identifying the structure and parameters of the system has a mixed discrete and continuous nature, which hampers the application of classical distributed schemes. Here, we propose a method that overcomes this limit by adopting a probabilistic reformulation of the model structure selection problem.