Federated Explainable AI for Privacy-Preserving Population Segmentation in Distributed Healthcare Systems

Abstract

Abstract:

Healthcare data complexity and sensitivity create major obstacles for traditional data analysis methods due to the presence of strict privacy regulations. Federated Learning (FL) working together with Explainable Artificial Intelligence (XAI) presents an innovative approach to enable privacy-protected population segmentation throughout dispersed healthcare networks. Through investigating FL and XAI integration this paper demonstrates how electronic health record (EHR) usage can be both collaborative and transparent while maintaining ethical standards without violating data ownership rights or patient confidentiality. The research evaluates existing federated models while recognizing issues like data diversity and network delays and underscores XAI's contribution to model interpretation. The paper stresses the critical need for collaborative efforts between institutions while highlighting the requirement for robust infrastructure protection against malicious threats and the opportunities provided by cutting-edge methods including homomorphic encryption and blockchain. The results demonstrate that FL-XAI frameworks can greatly improve public health insights derived from data while allowing medical interventions to become more fair, accurate, and transparent. Clinical environments require standardized data harmonization protocols alongside scalable federated architectures investments to achieve broader real-world application.