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The paper introduces XCal-FL, a federated learning algorithm that dynamically calibrates differential privacy noise using three signals—prediction logit variations, counterfactual margins, and saliency concentration—to improve both predictive accuracy and explanation fidelity. Experiments on medical imaging datasets demonstrate that XCal-FL outperforms static-noise and state‑of‑the‑art adaptive DP methods, achieving over 10% better accuracy and up to fivefold higher explanation fidelity while using privacy budgets more efficiently. The study highlights that explanation fidelity behaves non‑linearly with privacy loss, indicating that explainability is a separate dimension of the privacy trade‑off.
The paper introduces XCal-FL, a federated learning algorithm that dynamically calibrates differential privacy noise using three signals—prediction logit variations, counterfactual margins, and saliency concentration—to improve both predictive accuracy and explanation fidelity. Experiments on medical imaging datasets demonstrate that XCal-FL outperforms static-noise and state‑of‑the‑art adaptive DP methods, achieving over 10% better accuracy and up to five‑fold higher explanation fidelity while using privacy budgets more efficiently. The study reveals that explanation fidelity behaves non‑linearly with privacy loss, indicating that explainability is a separate dimension of the privacy trade‑off that cannot be inferred from utility alone.
arXiv:2601. 19618v2 Announce Type: replace-cross Abstract: Differential privacy protects the patients whose images train medical imaging models, but it lowers diagnostic accuracy, and the initialization is the strongest known remedy.
The paper introduces Differential Privacy Representation Geometry for Medical Imaging (DP‑RGMI), a framework that interprets differential privacy as a structured transformation of representation space. DP‑RGMI decomposes performance loss into encoder geometry—measured by representation displacement and spectral effective dimension—and task‑head utilization, quantified by the gap between linear‑probe and end‑to‑end utility. Across 594,000 chest X‑ray images from four datasets, the study finds that differential privacy consistently creates a utilization gap even when linear separability remains, while displacement and spectral dimension vary non‑monotonically with initialization and dataset, indicating that privacy alters representation anisotropy rather than uniformly collapsing features.
arXiv:2604.12411v2 Announce Type: replace Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit ove...
arXiv:2608. 04147v1 Announce Type: cross Abstract: Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases.