Variational Encrypted Model Predictive Control

TL;DR

This paper introduces VEMPC, a secure and efficient encrypted model predictive control method that reduces online encrypted computation by reformulating the problem into a sampling-based estimator, ensuring privacy and scalability.

Contribution

The paper presents a novel VEMPC protocol that operates solely on encrypted polynomial operations, enabling privacy-preserving control with reduced computational complexity.

Findings

Reduces online encrypted computation via sampling-based estimator.

No additional communication or decryption needed during execution.

Simulation confirms practical effectiveness and scalability.

Abstract

We develop a variational encrypted model predictive control (VEMPC) protocol whose online execution relies only on encrypted polynomial operations. The proposed approach reformulates the MPC problem into a sampling-based estimator, in which the computation of the quadratic cost is naturally handled by tilting the sampling distribution, thus reducing online encrypted computation. The resulting protocol requires no additional communication rounds or intermediate decryption, and scales efficiently through two complementary levels of parallelism. We analyze the effect of encryption-induced errors on optimality, and simulation results demonstrate the practical applicability of the proposed method.