Combining Federated Learning and Control: A Survey

TL;DR

This survey reviews how federated learning can be integrated with control systems to improve privacy, scalability, and adaptability in nonlinear control applications, highlighting current methods and future potential.

Contribution

It provides a comprehensive overview of the state-of-the-art in combining federated learning with control, emphasizing methodological benefits and application areas.

Findings

Federated learning enhances privacy and scalability in control applications.

Combining FL with control enables adaptive and distributed decision-making.

The survey identifies key challenges and future directions in the field.

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 modeling over controller design, focusing on adaptive control, to knowledge transfer in multi-agent decision-making systems.