FedBound a boundary aware optimization strategy for federated medical image segmentation under non IID data

Abstract Medical image segmentation is essential for computer-aided diagnosis and treatment planning. Privacy constraints impede centralized training using medical images from diverse healthcare organizations. Federated learning (FL) is developed to facilitate collaborative model training without sharing patient information. However, the statistical heterogeneity of data (non-IID distribution) among clients adversely impacts the segmentation task, particularly the quality of segmentation at object borders, which remains insufficiently explored. This paper presents a systematic analysis of boundary-sensitive medical image segmentation under heterogeneous federated learning conditions. We propose FedBound , a lightweight boundary-aware optimization technique that emphasizes contour areas during local training without increasing communication overhead. Additionally, we examine the impact of multiscale feature representations using an ASPP-based federated framework termed FedASPP . Experiments were conducted on the ISIC 2018 dataset for skin lesion segmentation, employing a Dirichlet non-IID distribution across 100 federated clients. The findings indicate that FedBound enhances boundary quality, reduces the HD95 score, and maintains high Dice and IoU coefficients across various segmentation architectures. Furthermore, FedBound reduces performance variability among clients, demonstrating improved stability in heterogeneous federated environments.