Federated multi scale vision transformer with adaptive client aggregation for industrial defect detection

Sailaja Are ¹, N Gopala Krishna ², Jagadeesh Thati ³

⁰Department of Computer Science Engineering, Tirumala Engineering College, Narasaraopet, AP, 522601, India. sailuare@gmail.com.

Scientific Reports +

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October 1, 2025

View abstract on PubMed

Summary

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.

Keywords:

Adaptive client aggregation (ACA) Federated learning Industrial defect detection Multi-scale vision transformer (MSVT) Smart manufacturing contrastive feature alignment (CFA)