Privacy-preserving federated machine learning modeling and predictive control of heterogeneous nonlinear systems.

Privacy preservation in data-driven modeling of a heterogeneous nonlinear system with multiple subsystems has become vitally important since communication data is vulnerable to cyber-attacks. This work develops a federated learning (FL) modeling and model predictive control (MPC) method for heterogeneous nonlinear systems with multiple subsystems to address privacy preservation and heterogeneity issues. The FL framework with two different neural network structures is developed to aggregate the submodels trained locally for the subsystems into a global model without sharing the local data with each other. Subsequently, a theoretical bound on the generalization error of the FL models is established using information theory. The closed-loop stability criteria of heterogeneous nonlinear systems under the FL-based MPC scheme are developed in an information-theoretical manner. Finally, a chemical process network is applied to demonstrate the effectiveness of the proposed FL modeling and FL-based MPC methods. • Privacy-preserving federated machine learning frameworks for distributed systems. • Guaranteed data privacy through distribution of learning process. • Theoretical results on generalization performance of federated learning models. • Desired modeling accuracy and data privacy shown in a chemical process example. [ABSTRACT FROM AUTHOR]