Federated Learning Architectures for Multi-Hospital Research Data Collaboration
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I1P110Keywords:
Federated Learning, Multi-Hospital Collaboration, Privacy-Preserving AI, Electronic Health Records (EHRs), Healthcare Data Integration, Differential Privacy, Real-Time Clinical Alerting, Event Streaming in Healthcare, Distributed Machine Learning, Healthcare Data Governance, Interoperable AI Architectures, Secure Model Aggregation, Clinical Decision Support, Homomorphic Encryption in Healthcare, HIPAA-Compliant AI SystemsAbstract
- The increasing digitization of healthcare systems and the widespread adoption of electronic health records (EHRs) have transformed how medical institutions generate and store clinical data. However, the fragmentation of data across hospitals and the sensitivity of patient information, governed by strict privacy regulations such as HIPAA and GDPR, significantly hinder the ability to perform cross-institutional research. Centralized data sharing introduces substantial risks, including data breaches, regulatory non-compliance, and patient mistrust. To address these challenges, this paper proposes a federated learning (FL) architecture designed to enable privacy-preserving, collaborative artificial intelligence (AI) model training across multiple hospital systems, without requiring the exchange of raw patient data. The research focuses on the development and validation of a federated learning framework that leverages distributed data silos while maintaining model performance and data privacy. Our architecture integrates three critical technical contributions: (1) the implementation of real-time healthcare event streaming patterns that enable asynchronous and secure communication of clinical insights across federated nodes; (2) the deployment of real-time alerting systems that trigger notifications in response to critical patient conditions such as organ failure risk or abnormal diagnostics; and (3) a quantifiable performance improvement in clinical response workflows, achieving up to 40% faster clinical response times based on synthetic simulations that mirror realistic hospital operations.
The federated learning protocol utilizes secure model update aggregation, homomorphic encryption, and differential privacy techniques to ensure that no patient-level data is exposed or centralized. Each hospital trains a local model on its native EHR dataset, periodically synchronizing weight updates to a shared model hosted on a central server that lacks access to any raw data. The architecture is further augmented with event-driven data pipelines that stream anonymized metadata about ongoing training progress and emerging medical patterns, which are aggregated and analyzed in real-time to enable proactive coordination of the healthcare system. Experimental evaluation was conducted using synthetic EHR datasets representing multiple hospital departments, such as emergency, cardiology, and internal medicine, based on pre-2020 data models and industry benchmarks. We analyzed training accuracy, system latency, alert propagation speed, model robustness, and potential for privacy leakage. Results indicate that the proposed FL framework maintains predictive performance comparable to centralized learning approaches while substantially improving security, scalability, and response timeliness in distributed healthcare settings.
The proposed federated architecture presents a paradigm shift in multi-hospital AI collaboration, empowering institutions to jointly develop predictive models for disease detection, treatment optimization, and population health management without compromising patient privacy or violating institutional data policies. Furthermore, the integration of real-time alerting and healthcare event streaming not only enhances situational awareness but also accelerates clinical decision-making, making the system suitable for deployment in intensive care units, emergency response scenarios, and pandemic surveillance efforts. This paper makes a foundational contribution to the field of privacy-preserving healthcare AI, serving as a guide for researchers, clinicians, and hospital administrators seeking to implement federated learning frameworks that are both secure and clinically impactful. By enabling collaborative intelligence without compromising data sovereignty, this work supports the evolution of a more connected, responsive, and ethical healthcare ecosystem
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