Federated Recursive System Identification and Fast Scheduling Method

Abstract

Federated learning is expected to be applied to many problems in control, given the increasing interest in data-driven control. We propose two federated system identification schemes to estimate parameters using observational data from multiple clients without transmitting the data. When performing federated learning, scheduling becomes necessary due to the system's instability and communication. Therefore, we also propose a fast scheduling method with its theoretical performance guarantee. We conduct experiments on federated recursive system identification using several scheduling algorithms, and investigates the accuracy and convergence speed.