Industrial defect detection for data privacy and domain differences: Based on federated learning and unsupervised domain adaptation
Chen RenFei, Li Zhongwen · Expert Systems with Applications · 2026
Industrial defect detection faces challenges from domain shifts in unsupervised domain adaptation (UDA) and data privacy in federated learning (FL). This study proposes a novel industrial defect detection method based on lightweight UDA and FL participant selection optimization (IDD-LUDA-FL), a lightweight UDA-FL framework for robust cross-domain defect detection. This method leverages MobileNet V4 for efficient feature extraction, dynamic feature pyramid network (DYFPN) for multi-scale processing, and quantized convolutional neural networks (Quantized-CNN) for model compression, while optimizing FL participant selection using Shapley values and second-order Markov chains to enhance privacy and mitigate attacks like label shuffling. Extensive studies on four industrial datasets show improved performance across a variety of source-target adaptation scenarios. Compared to other state-of-the-art methods, the proposed method achieves an average accuracy of 91%. Inference speed improves by over 50%, with detection speed reaching 58.6f/s. The holistic integration of lightweight UDA and secure FL mechanisms enables effective handling of data heterogeneity and adversarial threats, validating its utility for practical industrial applications. This work advances multi-domain defect detection, providing a foundation for scalable, privacy-preserving AI in manufacturing.