A Federated Transfer Learning Framework with Multi-Scale Aggregation for Surface Defect Classification in IIoT

With the rapid development of cutting-edge technologies such as software-defined networking, edge computing, and deep learning (DL), the application of the field of Industrial Internet of Things (IIoT) has been deepening, especially in the areas of fault diagnosis, defect detection, and production management, which has shown great potential. Federated learning (FL) is a collaborative model training approach that allows multiple clients to work together while maintaining data privacy. This method is particularly useful for DL methods in industrial surface defect classification, which often require a large amount of training data that can be hard to gather due to its distributed nature across various sources. However, the aggregated model in federated learning may not perform well when there is a discrepancy between the training dataset (source domain) and the testing dataset (target domain), as well as when individual users face data scarcity. To counter these challenges, we propose a novel federated transfer learning framework with multi-scale aggregation (FTL-MSA) for surface defect classification in the IIoT system. A dynamic central loss function, which takes into account both intra-instance and inter-instance contrasting, is proposed to enhance the model's accuracy. Furthermore, we introduce a multi-scale model aggregation technique for FL. This technique considers the distances between the source domain and the target domain at multiple scales, which utilizes the Jensen-Shannon distance for statistical consistency, and the cosine distance for directional consistency, thereby effectively mitigating the impacts of domain differences. Empirical validation on two public steel defect datasets shows that our FTL-MSA framework outperforms state-of-the-art methods, achieving accuracy improvements of 3.12%-12.51%.