With the rapid development of pre-trained language models, data privacy has become a critical concern. Federated Parameter-Efficient Fine-Tuning has emerged as an effective solution, preserving privacy while controlling computational and communication costs. However, the joint participation of data owners in the training process makes it vulnerable to backdoor attacks, which drives our focus on backdoor attacks in the Federated Parameter-Efficient Fine-Tuning. Experiments show that using efficient fine-tuning methods to freeze a large number of parameters does impact the success rate of backdoor attacks. Specifically, when parts near the output layer are frozen, the success rate of the backdoor attack significantly decreases, while the main task still converges normally. Based on these findings, we propose a new backdoor attack method: Frozen Layer Adversarial Sample-based Enhancement method. This method first generates adversarial examples that manipulate the output of frozen layers to target a specific class. Then, the trainable parameters are fine-tuned to generate these adversarial examples when backdoor data is input. Our experiments on GLUE text classification and CIFAR-10 image classification demonstrate that even when the server freezes parameters near the output layer, our method ensures a high success rate for backdoor attacks while maintaining stealth.