Federated multi scale vision transformer with adaptive client aggregation for industrial defect detection
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Fed-MSVT, a federated learning model for industrial defect detection. It enhances accuracy and privacy by using multi-scale vision transformers and adaptive client aggregation for smart manufacturing.
Area Of Science
- Computer Vision
- Machine Learning
- Industrial Automation
Background
- Traditional deep learning for defect detection requires centralized data, posing privacy risks and limiting adaptability in distributed manufacturing.
- Existing federated learning methods may not adequately address data quality and domain shift issues across diverse industrial sites.
Purpose Of The Study
- To develop a privacy-preserving and adaptive federated learning framework for industrial defect detection.
- To enhance the accuracy and robustness of defect detection in distributed manufacturing environments.
Main Methods
- Proposing Fed-MSVT, a Federated Multi-Scale Vision Transformer with Adaptive Client Aggregation.
- Utilizing multi-scale Vision Transformers (MSVTs) for comprehensive defect feature extraction.
- Implementing an Adaptive Client Aggregation (ACA) mechanism for dynamic model weighting.
- Employing a Contrastive Feature Alignment (CFA) module to reduce domain discrepancies.
Main Results
- Fed-MSVT demonstrates superior accuracy and robustness in detecting diverse industrial defects.
- The Adaptive Client Aggregation (ACA) mechanism effectively handles variations in client data quality and domain shifts.
- Contrastive Feature Alignment (CFA) improves model generalization across different manufacturing sites.
- Evaluations show enhanced performance compared to existing federated learning approaches.
Conclusions
- Fed-MSVT offers a scalable, privacy-preserving solution for real-time industrial defect detection.
- The proposed adaptive aggregation and feature alignment strategies are crucial for effective federated learning in manufacturing.
- This approach supports the development of adaptive and intelligent smart manufacturing systems.

