Privacy-Preserving and Explainable Federated Learning for Multi-Hospital Clinical Decision Support: A Narrative Review and Proposed Framework

Jobayar Alom; Md Tanzimul Islam; Md Shahriar Masud; Sudip Saha

doi:10.25163/primeasia.5110816

Journal of Primeasia

Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353

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Volume 5 Number 1 2024

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Privacy-Preserving and Explainable Federated Learning for Multi-Hospital Clinical Decision Support: A Narrative Review and Proposed Framework

Abstract 1. Introduction 2. Review Methodology 3. Federated Learning Approaches in Multi-Hospital Healthcare Settings 4. Privacy-Preserving Mechanisms: Differential Privacy, Secure Aggregation, and Homomorphic Encryption 5. Explainable AI for Clinical Trust and Interpretability 6. Blockchain-Enabled Auditability in Federated Systems 9. Challenges and Limitations Across the Literature 10. Future Directions Conclusion Acknowledgements Author Contributions Competing Financial Interests References

Jobayar Alom¹, Md Tanzimul Islam²*, Md Shahriar Masud², Sudip Saha³

+ Author Affiliations

Journal of Primeasia 5 (1) 1-9 https://doi.org/10.25163/primeasia.5110816

Submitted: 14 January 2024 Revised: 27 February 2024 Accepted: 04 March 2024 Published: 06 March 2024

Abstract

Background: Artificial intelligence holds considerable promise for clinical decision support, yet adoption across hospitals remains constrained by two persistent obstacles — the legal and ethical barriers to sharing patient data, and the limited interpretability of many high-performing models. Federated learning (FL) has emerged as a means of training models across institutions without centralizing raw data, while explainable AI (XAI) offers a partial remedy to the "black box" problem that complicates clinical trust.

Methods: This narrative review synthesizes peer-reviewed literature, predominantly published between 2018 and 2023, addressing the intersection of FL, privacy-preserving computation (differential privacy, secure aggregation, homomorphic encryption), explainability methods (SHAP, LIME, attention mechanisms), and blockchain-based auditability in healthcare contexts. Sources were identified through targeted database searches and citation tracking rather than a fully systematic protocol, consistent with the narrative review format.

Results: The reviewed literature suggests that FL can achieve competitive predictive performance while keeping patient data institution-bound, that privacy-preserving mechanisms introduce measurable but often manageable accuracy trade-offs, and that XAI methods meaningfully improve clinician engagement with model outputs. Comparatively few studies, however, integrate all three elements — privacy, explainability, and blockchain-based auditability — within a single validated multi-hospital framework.

Conclusion: Building on these patterns, we propose PPFBXAIO, a conceptual framework integrating privacy-preserving FL, explainable AI, and blockchain logging for multi-hospital clinical decision support. The framework remains theoretical pending empirical validation but offers a structured direction for future implementation and testing.

Keywords: Federated Learning; Explainable Artificial Intelligence; Privacy-Preserving Computation; Clinical Decision Support; Blockchain in Healthcare

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Privacy-Preserving and Explainable Federated Learning for Multi-Hospital Clinical Decision Support: A Narrative Review and Proposed Framework

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