Privacy-preserving federated learning for robust approximate MPC

Approximate model predictive control based on imitation learning methods enables realtime implementation of optimal constrained control even for large-scale systems under uncertainty. Training the underlying neural networks often requires large datasets of, which can be challenging for a single process operator to gather. A federated learning scheme, where multiple operators combine smaller datasets, could alleviate this issue. Yet, off-the-shelf federated learning may conflict with privacy requirements of the participants. In this paper, we present a collaborative federated learning scheme for robust approximate model predictive control. To protect data privacy with respect to the central computing server, we integrate homomorphic encryption, allowing for encrypted learning.