Summary of Combining Federated Learning and Control: a Survey, by Jakob Weber et al.
Combining Federated Learning and Control: A Survey
by Jakob Weber, Markus Gurtner, Amadeus Lobe, Adrian Trachte, Andreas Kugi
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The survey combines Federated Learning (FL) with control techniques to enhance adaptability, scalability, generalization, and privacy in nonlinear control applications. By leveraging FL’s distributed approach to model training, the method preserves data privacy while reducing network bandwidth requirements. The paper provides an overview of state-of-the-art concepts, discussing benefits and expected applications such as adaptive control and knowledge transfer in multi-agent decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey combines two powerful techniques: Federated Learning (FL) and control methods. FL helps keep data private by keeping it on local devices, while control methods help systems adapt to changing situations. By combining these approaches, the paper shows how to make control systems more adaptable, scalable, and generalizable while preserving privacy. |
Keywords
» Artificial intelligence » Federated learning » Generalization
