Federated learning framework for privacy-preserving defect recognition across distributed additive manufacturing networks

This paper presents a privacy-preserving federated learning framework for distributed additive manufacturing (AM) defect recognition, enabling joint training across multiple facilities without exchanging raw production data. The framework combines personalized federated learning with differential privacy to maintain high detection performance comparable to centralized training. A U-Net architecture with global encoders and site-dependent local decoders is used for semantic segmentation, adapting to varying data distributions across sites. To address class imbalance in defect detection, a multi-loss optimization approach is proposed, combining focal loss, dice loss, and weighted cross-entropy. Differential privacy is applied through gradient perturbation with adaptive noise calibration, ensuring privacy guarantees with ε = 1.0. Experimental results from eight manufacturing sites, with 45,680-layer images, show an accuracy of 91.8% and an F1-score of 0.889, with only a 0.6% drop compared to centralized training. Sites with limited local data experience 21–24 percentage points improvement over isolated training and a 98.7% reduction in communication overhead. Practical implementation yields 89.2% defect recall and 86.1% accuracy, demonstrating the effectiveness of the framework for industrial quality control.