Federated Learning Under Non-IID Patient Distributions for Malaria Diagnosis in Low-Connectivity Clinics: A Robustness Evaluation of Differential Privacy Budgets

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Federated learning promises to train malaria diagnostic models across clinics without centralising patient images, but two design choices interact in ways that current evaluations do not characterise: the non-IID partition of patient data across sites, and the differential privacy budget spent on each round of aggregation. the present analysis treats the privacy budget as a design parameter whose admissible range depends on the heterogeneity of the site-level label and feature distributions, and it derives the robustness consequences of that dependence. The analysis is methodological and theoretical rather than empirical: no patient images were collected, and no federated model was trained. Instead, the paper formalises the non-IID federated objective, defines a robustness criterion based on the worst-site sensitivity of the aggregated model, and derives a bound linking the privacy budget, the number of participating clinics, and the between-site divergence of the diagnostic distribution. The bound is then used to construct an evaluation protocol that a low-connectivity clinic network could execute with its own data. The central result is that the privacy budget cannot be selected independently of the non-IID structure: for a fixed robustness target, the admissible budget contracts as between-site divergence grows, and the contraction is steeper than the standard composition analysis predicts.

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