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The rapid evolution of artificial intelligence (AI) in healthcare necessitates robust frameworks to manage cross-institutional analytics while preserving data privacy and governance integrity. This conceptual systems research article proposes the federated analytics governance lattice (FAGL), a novel architecture that orchestrates intelligence across distributed healthcare institutions. FAGL integrates federated learning principles with governance mechanisms to facilitate secure, collaborative analytics without centralized data aggregation. The framework delineates layers for data sovereignty enforcement, intelligence orchestration, and compliance monitoring, incorporating feedback topologies for adaptive governance. Theoretical analysis explores risk-propagation models, decision-confidence formulations, and governance-load estimations to underscore the system’s theoretical underpinnings. By synthesizing literature on clinical AI architectures, interoperability frameworks, and decision-support pipelines, this work highlights how FAGL addresses challenges in EHR intelligence ecosystems and in workflow integration. The architecture emphasizes theoretical constructs to mitigate biases, ensure ethical AI deployment, and optimize cross-institutional synergies. Ultimately, FAGL offers a blueprint for scalable, privacy-preserving healthcare analytics that fosters innovation in multi-site clinical environments. This study contributes to the discourse on AI governance by providing a unique lattice-based topology that balances autonomy with collective intelligence, paving the way for future theoretical explorations in federated healthcare systems.

Diabetic retinopathy is a leading cause of preventable blindness, with fundus photography commonly used for early detection and severity grading, while deep learning models have shown strong performance in classification but require large, diverse multi-center datasets that are difficult to obtain due to privacy and regulatory restrictions. Because fundus images are protected health information, hospitals cannot share data, resulting in isolated datasets that limit model generalizability across different populations, imaging devices, and clinical settings. To overcome this limitation, a hybrid framework combining federated learning with homomorphic encryption is proposed, allowing multiple hospitals to collaboratively train a shared model without exchanging raw images or plaintext gradients. Each institution performs local training and transmits only encrypted model updates to a central server for secure aggregation, ensuring that patient data remains fully protected while still enabling global model improvement. This approach also mitigates gradient leakage and reconstruction attacks, supports compliance with regulations such as HIPAA and GDPR, and enables scalable, fault-tolerant deployment across heterogeneous healthcare systems, ultimately providing a privacy-preserving pathway for robust multi-center diabetic retinopathy detection.