Federated learning (FL) allows clients to train a deep learning model collaboratively while maintaining their private data locally. One challenging problem facing FL is that the model utility drops significantly once the data distribution gets heterogeneous, or non-i.i.d, among clients. A promising solution is to personalize models for each client, e.g.,keeping some layers locally without aggregation, which is thus called personalized FL. However, previous personalized FL often suffer from suboptimal utility because their choice of layer personalization is based on empirical knowledge and fixed for different datasets and distributions. In this work, we design PFedEdit, the first federated learning frame work that leverages automated model editing to optimize the choice of personalization layers and improve model utility under a variety of data distributions including non-i.i.d. The high-level idea of PFedEdit is to assess the effectiveness of every global model layer in improving model utility on local data distribution once edited, and then to apply edits on the top-k most effective layers. Our evaluation shows that PFedEdit outperforms six state-of-the-art approaches on three benchmark datasets by 6% on the model’s performance on average, with the largest accuracy improvement being 26.6%. PFedEdit is open-source and available at this repository: