DP-SimAgg claims per-round (epsilon, delta)-DP for federated brain tumor segmentation by adding Gaussian noise after similarity-weighted aggregation, but the noise scale relies on an empirically estimated sensitivity that is not a proven bound.
Machine learning for medical imaging: Methodological failures and recommendations for the future,
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Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation
DP-SimAgg claims per-round (epsilon, delta)-DP for federated brain tumor segmentation by adding Gaussian noise after similarity-weighted aggregation, but the noise scale relies on an empirically estimated sensitivity that is not a proven bound.