An ADMM-based approach for multi-class recursive parameter estimation

Due to the increasing popularity of cloud-based architectures, it is of paramount importance to understand how to benefit from shared information for solving collaborative estimation problems and exploit the additional computational resources available. Meanwhile, it is crucial to devise solutions that allow connected devices to retain private data and to carry out the desired tasks on their own, when disconnected from the cloud.In this paper, we present a cloud-aided iterative solution for multi-class parameter estimation for a set of mass-produced devices. The method exploits the similarity between systems operating under comparable conditions and their connection to the cloud, while allowing devices to retain and process raw data privately. The effectiveness of the strategy is assessed on a numerical example, showing its potential.