A federated evolving computing architecture for fault inspection in textile manufacturing

• A novel federated architecture for industrial fault inspection. • Alternating learning maintains specialised and collective knowledge. • Prototype calibration enhances the detection of rare but critical faults. • Variational consolidation mitigates catastrophic forgetting in adaptive systems. This paper introduces Federated Evolving Computing Architecture (FECA), a novel approach for fault inspection in textile manufacturing that addresses three critical challenges in distributed environments: statistical heterogeneity, class imbalance, and catastrophic forgetting. The architecture comprises three innovative components: Alternating Federated Evolving Learning (AFEL), which enables distributed inspection nodes to maintain specialised expertise while benefiting from collective knowledge; Prototypical Imbalance Calibration (PIC), which enhances the detection of rare but critical faults by calibrating prototype representations of minority classes; and Variational Consolidation for Adaptive Retention (VCAR), which mitigates catastrophic forgetting through a dual mechanism that maintains probabilistic representations of prototype distributions while selectively preserving critical parameters. The proposed architecture provides a robust framework for continuous improvement of industrial inspection systems without compromising production continuity, offering a promising solution for quality control in modern textile manufacturing.