Large language models are known for encoding a vast amount of factual knowledge, but they often become outdated due to the ever-changing nature of external information. A promising solution to this challenge is the utilization of model editing methods to update the knowledge in an efficient manner. However, the majority of existing model editing techniques are limited to monolingual frameworks, thus failing to address the issue of cross-lingual knowledge synchronization for multilingual models. To tackle this problem, we propose a simple yet effective method for cross-lingual model editing, which trains multilingual patch neuron (MPN) to encode cross-lingual knowledge. This approach can be easily adapted to existing approaches to enhance their cross-lingual editing capabilities. We conduct experiments on the XNLI dataset and a self-constructed XFEVER dataset. Experimental results demonstrate that the proposed method achieves improved performance in cross-lingual editing without excessive modifications on the original methodology, thereby showcasing its user-friendly characteristics.