Abstract:
Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products...Show MoreMetadata
Abstract:
Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products, processes, and applications with limited training data while preserving data privacy. In this article, a new federated transfer learning framework, federated transfer learning for cross-domain prediction, is proposed to address the challenges of data scarcity and data privacy faced by most machine learning approaches in modern smart manufacturing with cross-domain applications. The framework architecture consists of a central server and several groups of smart devices, where each group handles a different application. The existing applications can share their knowledge through the central server as base models, while new applications can convert a base model to their target-domain models with limited application-specific data using a transfer learning technique. Meanwhile, the federated learning scheme is deployed within a group to further enhance the accuracy of the application-specific model. The integrated framework allows model sharing across the central server and different smart devices without exposing any raw data and, hence, protects the data privacy. Two public datasets, COCO and PETS2009, which represent the source and target applications, are employed for evaluations. The simulation results show that the proposed method outperforms two state-of-the-art machine learning approaches by achieving better learning efficiency and accuracy.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 18, Issue: 6, June 2022)
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- IEEE Keywords
- Index Terms
- Transfer Learning ,
- Smart Manufacturing ,
- Federated Transfer Learning ,
- Cross-domain Prediction ,
- Training Data ,
- Machine Learning Approaches ,
- Data Privacy ,
- Efficient Learning ,
- Smart Devices ,
- Computational Intelligence ,
- Target Application ,
- Central Server ,
- Federated Learning ,
- Learning Accuracy ,
- Limited Training Data ,
- Device Group ,
- Architectural Framework ,
- Transfer Learning Framework ,
- Deep Learning ,
- Convolutional Neural Network ,
- Source Domain ,
- Individual Devices ,
- Target Domain ,
- Object Detection ,
- Data Privacy Protection ,
- Multiple Devices ,
- Limited Amount Of Data ,
- Pedestrian Detection ,
- Centralized Approach ,
- Communication Overhead
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Transfer Learning ,
- Smart Manufacturing ,
- Federated Transfer Learning ,
- Cross-domain Prediction ,
- Training Data ,
- Machine Learning Approaches ,
- Data Privacy ,
- Efficient Learning ,
- Smart Devices ,
- Computational Intelligence ,
- Target Application ,
- Central Server ,
- Federated Learning ,
- Learning Accuracy ,
- Limited Training Data ,
- Device Group ,
- Architectural Framework ,
- Transfer Learning Framework ,
- Deep Learning ,
- Convolutional Neural Network ,
- Source Domain ,
- Individual Devices ,
- Target Domain ,
- Object Detection ,
- Data Privacy Protection ,
- Multiple Devices ,
- Limited Amount Of Data ,
- Pedestrian Detection ,
- Centralized Approach ,
- Communication Overhead
- Author Keywords