Literature Database Entry

adamek2025privacy

Joshua Adamek, Janis Adamek, Moritz Schulze Darup and Sergio Lucia, "Privacy-preserving federated learning for robust approximate MPC," Elsevier IFAC-PapersOnLine, vol. 59 (6), pp. 1–6, 2025.

Abstract

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.

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Joshua Adamek
Janis Adamek
Moritz Schulze Darup
Sergio Lucia

BibTeX reference

@article{adamek2025privacy,
    author = {Adamek, Joshua and Adamek, Janis and Schulze Darup, Moritz and Lucia, Sergio},
    doi = {10.1016/j.ifacol.2025.07.112},
    title = {{Privacy-preserving federated learning for robust approximate MPC}},
    pages = {1--6},
    journal = {Elsevier IFAC-PapersOnLine},
    issn = {2405-8963},
    publisher = {Elsevier},
    number = {6},
    volume = {59},
    year = {2025},
   }
   
   

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Last modified: 2026-04-30