Multi-layer encrypted learning for distributed healthcare analytics.

The Internet of Medical Things (IoMT) enables continuous collection and transmission of healthcare data through interconnected networks of patient wearables and other devices. This capability transforms traditional healthcare systems into data-rich environments. However, this also brings privacy concerns because of the widespread distribution of health data across multiple healthcare systems. Such concerns, including data breaches and privacy violations, become paramount when aggregating data into a centralized location for analytical purposes. Therefore, this paper proposes a novel privacy-preserving framework designed with a three-layer protection mechanism for distributed healthcare analytics on encrypted data. This framework mitigates the risk of data breaches while balancing data privacy with model accuracy tradeoffs. First, fully homomorphic encryption (FHE) is introduced to encrypt healthcare data. This mechanism enables analytical computations while mitigating the risk of data breaches. Building on this, we develop a distributed FHE framework that eliminates the need for centralized data storage and supports iterative learning through continuous model updates as new data become available. Furthermore, we propose a distributed ensemble learning architecture that leverages parallel processing to accelerate the generation of consensus models for healthcare analytics. Experimental results from real-world intensive care unit (ICU) case studies show that the proposed framework effectively protects data privacy while maintaining the performance of analytical models. Moreover, compared with individual departmental models, the proposed privacy-preserving framework achieves the highest accuracy of 84.6%. These findings highlight the potential of a federated privacy-preserving framework to avoid centralized data storage and support collaborative analytics in data-rich healthcare environments.