Privacy Preserving Federated Learning with Blockchain and Explainable AI: A Governance-Aware Framework for Equitable Healthcare Analytics

Various hospitals and other healthcare organizations are keeping data of their patients in the form of organized and unorganized data. These databases can be shared among other hospitals, diagnostic centres, and research laboratories for further investigation and research. But due to some privacy issues, regulations, hospital policies, and competitive concerns, the data cannot be shared among all healthcare industries. Therefore, a federated learning approach with some security like blockchain, etc., has emerged as a practical approach to share data with other healthcare institutions, keeping their records private and safe. Due to having different approaches to collecting data by different healthcare organizations, the data become heterogeneous and create some problems for others, like statistical heterogeneity across institutional datasets, vulnerability of model aggregation to adversarial or low-quality participants, and limited interpretability for all healthcare organizations to interpret results. Also, it can violate government regulations. In this paper, we are proposing a simple conceptual framework or model that integrates a federated learning approach with blockchain technology and explainable artificial intelligence, considering government regulations to address heterogeneity and security of data to have good data analytics and interpretation. The main objective in this paper is to provide federated learning researchers, security researchers, and the healthcare industry a formatted, structured architecture or framework for building federated healthcare systems that can be secure, fair, auditable, and explainable.