  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.
