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.

翻译：本综述概述了将联邦学习（FL）与控制相结合以增强（非线性）控制应用中的适应性、可扩展性、泛化性和隐私性的研究。传统控制方法依赖于控制器设计模型，但实际场景通常需要在线模型重调或学习。FL提供了一种分布式模型训练方法，能够在分布式设备间实现协作学习，同时保护数据隐私。通过将数据保留在本地，FL减轻了隐私和安全方面的顾虑，同时降低了通信所需的网络带宽。本综述总结了将FL与控制相结合的最新概念与思想。进一步讨论了该方法论的优势，并最终详细概述了预期的应用领域，涵盖从动态系统建模到控制器设计（重点关注自适应控制），再到多智能体决策系统中的知识迁移。