Federated learning (FL) has emerged as a promising approach to facilitate collaborative model training while ensuring data privacy. FL faces challenges related to heterogeneity in data distributions, interpretability of model decisions, and optimization of model architectures across decentralized nodes. This paper proposes a framework that combines FL with neural architecture search (NAS) and explainable large language model (XLLM) to overcome these issues and improve clinical outcomes. We test this approach in three medical areas. NAS is used to discover optimized model architectures tailored to heterogeneous medical data across decentralized hospitals. XLLMs are employed to interpret and communicate complex decision-making processes. Experimental validation on benchmark datasets for each clinical use case indicates improvements in predictive accuracy and clinical relevance compared to conventional federated approaches.