SOFIA: Self-adaptive Optimization for Federated learning with Inpainting-based Anomaly-aware pretraining for robust defect detection

Visual defect detection is critical for quality control in modern manufacturing, where data are often distributed across multiple production sites and subject to strict privacy constraints. In industrial settings such as pharmaceutical production, these challenges are further amplified by limited defect samples and strict data-sharing restrictions, making centralized training impractical while standard federated learning struggles with heterogeneous data distributions and limited defect samples. We introduce SOFIA, a privacy-preserving federated defect detection framework that jointly addresses these challenges by combining client-adaptive hyperparameter optimization based on Horse Self-adaptive Optimization with object-oriented inpainting-based pretraining. The former adapts local training to client-specific data characteristics, while the latter generates semantically localized synthetic defects to improve representation learning under limited data. Experiments on pharmaceutical and metal surface defect detection benchmarks show that SOFIA improves detection performance over centralized and federated baselines. Ablation results further indicate that the two mechanisms play complementary roles: adaptive optimization improves federated training, while inpainting-based pretraining provides its largest benefit when integrated with the federated optimization framework. We further study how SOFIA behaves as the number of participating clients grows, finding consistent gains in performance and stability as more clients participate, suggesting favorable scalability as federation size increases. These findings highlight the importance of jointly addressing optimization and representation learning for scalable and reliable visual defect detection in distributed, privacy-constrained industrial settings.