Design Considerations and Guidelines
• identifies the key technological enablers ( i . e ., digitization ) needed to support the application of FedL solutions .
• proposes a set of characteristics that align use case scenarios with the benefits of a FedL approach in a smart manufacturing context .
• offers guidelines for FedL implementation and deployment in the context of use case scenarios in smart manufacturing .
The scope of this paper is to explore the design requirements and implementation strategies for the use of federated learning within the manufacturing domain . Specifically , this will provide insight into approaches for creating a collaborative ecosystem where organizations can mutually benefit from robust machine learning models . This requires making high-quality data sets that are typically beyond the reach of any single organization accessible to all participants in a secure and privacy preserving manner .
• Chapter 1 – Introduction
• Chapter 2 – Motivation
• Chapter 2 – Design considerations for FedL enabled collaborative ecosystem
• Chapter 3 – Value of collaborative-ecosystem : potential business models
• Chapter 4 – Guidelines for implementing FedL in smart manufacturing
• Chapter 5 – Use case implementation
• Chapter 6 – Conclusion
Manufacturing industry practitioners and applied researchers working towards realizing a collaborative ecosystem using federated learning .
• ML – Machine Learning
• FedL – As per Kairouz , 2021 , “ Federated Learning is a machine learning setting where many clients ( e . g ., mobile devices or whole organizations ) collaboratively train a model
20 March 2022