Trustworthy intelligent rooms: integrating blockchain, federated learning, and data-centric AI for healthcare 4.0

Abstract

Introduction: Intelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to poisoning attacks. Methods: This paper presents a novel, privacy-preserving architecture for Ambient Intelligence (AmI) that integrates Distributed Ledger Technology (DLT). Results: We explicitly note that while DLT does not preemptively prevent the generation of poisoned gradients, it provides an immutable, cryptographically secure audit trail. This ensures the trustworthiness and traceability of model updates for post-hoc detection, strict accountability, and targeted model rollbacks. Discussion: By fusing Data-Centric AI for quality assurance with a Blockchain-enabled FL framework, we propose a scalable, low-cost solution for real-time patient monitoring in diverse economic settings.