Split-Federated BERT With Adversarial Training for Edge Intrusion Detection

Pre-trained language models, represented by Bidi-rectional Encoder Representations from Transformers (BERT), show great potential for deep learning-based intrusion detection systems (IDS) due to their strong semantic modeling capability. However, the high cost of training and fine-tuning limits their applicability in large-scale and resource-constrained environments. To address this challenge, we propose a Split-Federated BERT framework with adversarial training for edge intrusion detection. The framework partitions BERT into an Embedding layer deployed at the edge and Transformer and Head layers hosted in the cloud, enabling collaborative training between edge devices and the cloud. At the edge, a conditional generative adversarial network (CGAN) integrated with BERT enhances traffic feature extraction. Guided by BERT, the generator adapts to local traffic distributions, improving sample coverage and feature representation. Edge devices perform local updates to the Embedding layer, while the cloud conducts high-dimensional semantic learning using BERT’s Transformer and Head layers. During federated aggregation, a multi-head attention mechanism is employed in the cloud to differentially weight model updates, ensuring distributional alignment and stable convergence. This design decouples edge-side adversarial enhancement from federated aggregation, reducing both computational and communication overhead. Experimental results on multiple authoritative datasets demonstrate that the proposed method consistently outperforms local deep learning, BERT, federated learning, and split learning baselines in precision, recall, and F1-score, while improving edge computational efficiency and communication cost.