The application of deep learning in chest X-ray diagnosis has demonstrated promising results in detecting multiple thoracic diseases. However, traditional centralized approaches face significant challenges, including limited generalization across hospitals with heterogeneous patient populations and imaging protocols, compounded by strict privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) that prevent data sharing between institutions. Although centralized deep learning approaches perform well and achieve high local accuracy on their predictions, they are often “black boxes,” which limits clinical trust and interpretability. This review examines existing explainable federated learning frameworks for chest X-ray diagnosis under heterogeneous data conditions. Current approaches enable decentralized training across non-independent and identically distributed (non-IID) hospital environments, utilizing robust aggregation strategies such as Federated Averaging (FedAvg) and Federated Proximal (FedProx) to address label, quantity, and feature skew. To establish clinical trust, explainable Artificial Intelligence (XAI) techniques, such as Gradient weighted Class Activation Mapping (Grad CAM) and SHapley Additive exPlanations (SHAP), have been incorporated to generate interpretable visual explanations. The reviewed frameworks are evaluated on classification performance, robustness under heterogeneity, and stability of generated explanations. However, this review reveals significant gaps: the types of heterogeneity are addressed in isolation, XAI evaluation remains largely qualitative, and explanation stability under non-IID conditions lacks rigorous validation. These findings collectively highlight the need for federated frameworks that unify heterogeneity handling across all its forms simultaneously rather than addressing each in isolation, quantitative XAI assessment, and validation of explanation consistency across diverse hospital environments to enable trustworthy and interpretable clinical deployment.