The Convergence of Data Virtualization and Federated Learning in Pharmaceutical Real-World Evidence (RWE) Generation: A Survey and Gap Research in Architectures, Tools, and Governance Challenges

Abstract

The generation of pharmaceutical Real-World Evidence (RWE) is a critical imperative for modern healthcare, yet it is hindered by two fundamental bottlenecks: (1) stringent privacy regulations, such as HIPAA, which preclude data centralization, and (2) pervasive, systemic data fragmentation within healthcare institutions.2 Federated Learning (FL) has emerged as the consensus solution for the privacy challenge, enabling model training on distributed data. Concurrently, Data Virtualization (DV) is the industry-standard solution for data fragmentation, providing a unified logical view of data silos. The current scientific literature, however, investigates these two solutions in parallel, failing to address the critical gap at their intersection. FL research implicitly operates on a "Unified Node Assumption," presuming each participating hospital has its data in a single, queryable repository, which is practically false. This paper bridges this gap by conducting a systematic survey of both domains and proposing the first architectural taxonomy of "Federated Learning on a Virtualized Data Layer" (FL-on-VD). Three novel architectural models are proposed and illustrated: (1) FL with Virtualized Query Pushing (FL-VQP), (2) FL on a Logical Data Mesh (FL-LDM), and (3) FL on a Centralized Virtual-View (FL-CVV). This paper analyzes the unique, second-order challenges this convergence creates in federated query optimization, semantic interoperability, and "double-blind" governance. It concludes that this unified FL-on-VD architecture represents the only viable and scalable path toward national-level RWE generation.