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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.11069 |
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| _version_ | 1866912118104653824 |
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| author | Weber, Jakob Gurtner, Markus Lobe, Amadeus Trachte, Adrian Kugi, Andreas |
| author_facet | Weber, Jakob Gurtner, Markus Lobe, Amadeus Trachte, Adrian Kugi, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Federated Learning and Control: A Survey Weber, Jakob Gurtner, Markus Lobe, Amadeus Trachte, Adrian Kugi, Andreas Machine Learning Systems and Control 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. |
| title | Combining Federated Learning and Control: A Survey |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2407.11069 |