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Privacy-Preserving Federated Learning System for Heart Disease Prediction

Okokon, Kate Edidiong, Sako, D.J.S., Anireh, V. I. E.

Abstract

Smart healthcare wearable devices generate physiological data that can support disease prediction and remote patient monitoring, but centralising such data increases privacy, governance, and patient-trust risks. This paper presents a privacy-preserving federated learning system for heart disease prediction. The system was implemented as a simulation- based Python system and combined data preprocessing, Dirichlet-based non-independent and identically distributed (non-IID) client partitioning, Federated Averaging, gradient clipping, and Gaussian noise addition and performance evaluation. The Cleveland Heart Disease dataset was used as wearable-representative healthcare data rather than live wearable data. Although the variables are relevant to wearable-assisted cardiovascular monitoring, the dataset was not collected directly from smart wearable devices. The evaluation was simulation- based and did not include physical wearable hardware, real edge devices, network latency, battery usage, memory constraints, or client dropout. Overall, the application interface provides presentation-level evidence that the proposed system is not only a static report output as it supports parameter selection, live process visibility, result inspection, visual evidence review, and prediction demonstration. This strengthens the practical demonstration of the simulation-based framework while preserving the reproducibility of the original experiment script and result files.

Keywords

Federated learningPrivacy PreservationSmart HealthcareWearable DevicesHeart DiseasePredictionDifferential PrivacySimulation.

References

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