Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For Disease Prediction On Non-IID EHRs. Specifically, PEARL Incorporates Disease Diagnostic Code Attention And Admission Record Attention To Extract Patient Embeddings From All EHRs. Then, PEARL Integrates Self-Supervised Learning Into A Federated Learning Framework To Train A Global Model For Hierarchical Disease Prediction. To Improve The Performance Of The Client Model, We Further Introduce A Fine-Tuning Scheme To Personalize The Global Model Using Local EHRs. During The Global Model Updating Process, A Differential Privacy (DP) Scheme Is Implemented, Providing A High-Level Privacy Guarantee. Extensive Experiments Conducted On The Real-World MIMIC-III Dataset Validate The Effectiveness Of PEARL, Demonstrating Competitive Results When Compared With Baselines.