Combining Federated Learning and Control: A Survey

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

Abstract

This survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real-world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state-of-the-art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi-agent decision-making systems.
OriginalspracheEnglisch
Seiten (von - bis)2503–2523
FachzeitschriftIET Control Theory and Applications
Volume18
Issue18
DOIs
PublikationsstatusVeröffentlicht - 12 Nov. 2024

Research Field

  • Complex Dynamical Systems

Fingerprint

Untersuchen Sie die Forschungsthemen von „Combining Federated Learning and Control: A Survey“. Zusammen bilden sie einen einzigartigen Fingerprint.

Diese Publikation zitieren