Combining Federated Learning and Control: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 143 of its 252 references that are in the map. First posted on arXiv 12 Jul 2024.
Builds on
143 of 252 references.
References · 143
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38 more
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