Privacy-preserving Federated Learning for Industrial Defect Detection Systems via Differential Privacy and Image Obfuscation

Artificial Intelligence (AI) has been widely used in manufacturing to detect defects. AI models utilize product images to distinguish whether a product is normal or abnormal. If attackers use the model inversion attack to attack AI models, the input images can be roughly restored, resulting in product information leakage. In this paper, we propose a Privacy-preserving Industrial Defect Detection System (PIDS), which includes three image obfuscation methods to hide input image information and uses them to train the model. Federated learning and differential privacy are also applied to ensure that sensitive data remains decentralized and secure, even during training. Federated learning allows the model to be trained across multiple local datasets without centralized data collection, thereby reducing the risk of data exposure. Differential privacy adds another layer of protection by adding randomness to the learning process, making it hard for attackers to extract sensitive information from the trained model. Experiments show that the proposed system can achieve a high accuracy level of 96.5% in defect image classification. Therefore, the proposed system can detect defects accurately and preserve product information in terms of data and models.