[2604.06518] Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.06518 (eess) [Submitted on 7 Apr 2026] Title:Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities Authors:Puja Saha, Eranga Ukwatta View a PDF of the paper titled Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities, by Puja Saha and Eranga Ukwatta View PDF HTML (experimental) Abstract:Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to generalize across clinical sites because of heterogeneity in imaging protocols and continuously evolving data distributions arising from differences in scanners, acquisition parameters, and patient populations. Federated learning offers a promising solution by enabling collaborative model training without sharing raw data. However, incorporating differential privacy into federated learning, while essential for privacy guarantees, often leads to degraded accuracy, unstable convergence, and reduced generalization. In this work, we propose an adaptive differentially private federated learning (ADP-FL) framework for medical image segmentation that dynamically adjusts privacy mechanisms to better balance the privacy-utility trade-off. The proposed approach stabilizes training, significant...