Learning Across Locked Doors: A Synthesis of Federated Learning Architectures for Cross-Hospital Medical Image Analysis

Abstract

Medical image analysis has been transformed by deep learning, yet the field’s progress is constrained by a structural paradox:
the models that would benefit most from large, diverse datasets are trained on data that privacy law, institutional policy, and
patient trust forbid from being pooled in one place. Federated learning has emerged as the leading response to this paradox,
allowing multiple hospitals to collaboratively train a shared diagnostic model while retaining every patient record within
its institution of origin. This paper presents an integrative literature review synthesizing twenty-two peer-reviewed and
preprint sources published between 2016 and 2025 to examine how federated learning has been adapted specifically to the
demands of cross-hospital medical imaging, where non-independent and identically distributed data, scanner and protocol
heterogeneity, and adversarial privacy risks complicate the optimistic promise of the original paradigm. Employing a thematic
synthesis methodology, the review codes the retrieved literature according to architectural approach, data heterogeneity
mitigation strategy, privacy-preserving mechanism, and clinical validation status, before proposing a conceptual framework
that positions federated imaging systems along two axes: statistical robustness to institutional heterogeneity and depth of
privacy assurance. The synthesis finds that federated frameworks incorporating explicit heterogeneity correction and formal
privacy guarantees, such as differential privacy or secure aggregation, more consistently approach the diagnostic performance
of centralized training than those relying on naive averaging alone, though real clinical deployment remains rare relative to
the volume of published methodological innovation. The paper concludes that federated learning constitutes a necessary but
not yet sufficient infrastructure for cross-hospital collaboration and outlines the standardization efforts required to close the
gap between technical feasibility and routine clinical adoption.