Open Access

ARTICLE

An Optimized Unsupervised Defect Detection Approach via Federated Learning and Adaptive Embeddings Knowledge Distillation

Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

1 School of Computer Science and Artificial Intelligence, Foshan University, Foshan, 528225, China
2 Foshan Wision Furniture Manufacturing Co., Ltd., Foshan, 528200, China
3 School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528225, China
4 Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, 528225, China

* Corresponding Author: Lufeng Luo. Email:

(This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)

Computers, Materials & Continua 2025, 84(1), 1839-1861. https://doi.org/10.32604/cmc.2025.064489

Received 17 February 2025; Accepted 17 April 2025; Issue published 09 June 2025

Abstract

Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator for defect detection, thereby enhancing the pre-trained model’s industrial imagery representation capabilities. (2) Knowledge distillation co-training stage: a multi-model feature knowledge distillation mechanism is introduced. Through feature-level knowledge transfer between the global model and historical local models, the current local model is guided to learn better feature representations from the global model. The approach prevents local models from converging to local optima and mitigates performance degradation caused by data heterogeneity. (3) Model parameter aggregation stage: participating clients utilize weighted averaging aggregation to synthesize an updated global model, facilitating efficient knowledge consolidation. Experimental results demonstrate that FADE improves the average image-level Area under the Receiver Operating Characteristic Curve (AUROC) by 7.34% compared to methods directly utilizing pre-trained models. In federated learning environments, FLAME’s multi-model feature knowledge distillation mechanism outperforms the classic FedAvg algorithm by 2.34% in average image-level AUROC, while exhibiting superior convergence properties.

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Keywords

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