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.

该论文概述了结合联邦学习（FL）和控制方法以增强非线性控制应用的适应性、可扩展性、泛化性和隐私性。通过保持数据本地化，FL在分布式设备上进行模型训练，实现协作学习并保护数据隐私，减少通信的网络带宽需求。该论文总结了结合FL和控制的最新概念和想法，进一步讨论了方法上的好处，最终详细概述了预期应用，从动态系统建模、控制器设计、自适应控制到多智能体决策系统中的知识传递。

结合联邦学习和控制: 一项调查