Split Learning With Local Epoch Regulation and Time-Aware Detection

Federated learning (FL) has become a popular approach in Edge AI for extracting valuable knowledge within edge computing (EC) systems. To enhance AI application performance, large-scale models have gained increasing attention due to their strong generalization capabilities. However, training and transmitting such models impose substantial computational and communication overhead on resource-constrained clients at the edge, and exchanging complete models may also compromise model privacy. To alleviate these burdens and safeguard privacy, split learning (SL) has been introduced by combining data and model parallelism. Although SL alleviates resource constraints, it still encounters efficiency and security challenges in EC environments, where heterogeneous clients can slow down training without enhancing accuracy, and malicious clients may manipulate model behavior. To address these challenges, we propose a novel SL framework, CoDefend, which integrates local epoch regulation and time-aware detection. Specifically, local epoch regulation dynamically assigns heterogeneous clients with appropriate local epoch numbers to improve training efficiency, while time-aware detection provides an effective detection window to identify clients' malicious manipulation to improve model security. Moreover, CoDefend jointly optimizes these two strategies by leveraging their interdependence to further improve SL performance. Extensive experiments on both simulated and real-world platforms using NVIDIA Jetson edge nodes demonstrate that CoDefend achieves approximately 2× faster training speed than baseline methods, while maintaining comparable model accuracy and effectively identifying malicious manipulations even under collusion.