Class-Anchor-Based Federated Continual Learning for Vehicle Part Recognition

Federated learning provides a feasible way to support collaborative vehicle part recognition without sharing raw data across distributed vehicle service nodes. However, real vehicle scenarios exhibit spatiotemporal heterogeneity, where client distributions vary and new vehicle models, parts, and viewpoints emerge over time. Such heterogeneity causes catastrophic forgetting and degrades knowledge retention in federated continual learning. To address this problem, this paper proposes FedACA (Federated Continual Learning with Adaptive Class Anchors), a class-anchor-based federated continual learning method for vehicle part image classification. The method uses a frozen Vision Transformer backbone and introduces scene-aware prompt adaptation, adaptive class anchors, non-parametric global prototype selection, and class-aware prompt fusion. These modules stabilize class semantics, reduce feature drift, and improve cross-client knowledge alignment in a data-local federated setting with raw-data non-sharing; however, FedACA does not provide formal privacy guarantees. Experiments on a self-constructed 67-class multi-view vehicle-part dataset and CIFAR-100 show that, under the task-aware class-masking protocol, FedACA achieves final-stage average accuracies of 97.34% ± 0.72% and 95.71% ± 0.63%, respectively, over three independent random seeds, outperforming the evaluated federated continual learning baselines. These results demonstrate that FedACA improves recognition performance and historical knowledge retention under spatiotemporally heterogeneous federated continual learning settings.