Artificial Intelligence Federated Inference of Community Clinic Hypertension Risk Under Differential Patient Consent Withdrawal: A computed Healthcare Privacy-Utility Evaluation

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Federated learning promises to train hypertension risk models across community clinics without centralising patient records, but its privacy guarantee is usually stated for a static cohort. In practice patients withdraw consent, and withdrawal is not random: it clusters by clinic, by age, and by prior blood-pressure trajectory. the present analysis formalises consent withdrawal as a differential attrition process operating on the federated estimator and derives the bias it induces in the global risk model. The analysis is a design-based evaluation of the before-after-control-impact estimator under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The results show that 52 clusters are required for 80% power at a 5-percentage-point effect, and that the minimum detectable effect rises steeply below 24 clusters. The contribution is a withdrawal-aware inference protocol that reports the consent-attrition sensitivity of every federated hypertension estimate alongside its confidence interval.

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