Adaptive Dual‐Model Federated Learning for Generalizable Brain Tumor Segmentation

ABSTRACT Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) is critical for diagnosis, treatment planning, and longitudinal monitoring. The development of robust and generalizable deep learning models for tumor segmentation is hindered by challenges such as data privacy, limited annotations, and domain variability across clinical institutions. To address these issues, we propose a dual‐model federated learning model for brain tumor segmentation that enables collaborative model training without sharing patient data. The model employs two specialized architectures: a Multi‐Scale Encoder U‐Net (MSE‐UNet) for fine‐grained, multi‐resolution feature extraction and a Residual Attention Transpose U‐Net (ART‐UNet) that leverages residual learning and dual attention mechanisms to enhance contextual sensitivity and robustness under non‐IID conditions. To ensure effective learning across distributed, heterogeneous clients, we introduce a Dual‐Model Architecture‐Aware Aggregation (DAAA) strategy, which performs independent, performance‐weighted aggregation of each architecture's updates. The proposed method is evaluated on two benchmark datasets, BraTS 2018 and TCGA‐LGG, demonstrating superior performance compared to several state‐of‐the‐art baselines. The model achieves Dice scores of 91.30% and 90.10% on BraTS and TCGA‐LGG, respectively, with improved IoU, sensitivity, and boundary precision. Ablation studies confirm that each component, including auxiliary supervision, architectural duality, and adaptive aggregation, contributes significantly to overall performance. Clinically, this framework offers a scalable and privacy‐preserving solution that can be integrated into real‐world healthcare systems without compromising patient data security. By enabling cross‐institutional collaboration and ensuring robust performance across diverse imaging protocols, the proposed model facilitates early and accurate tumor delineation, supporting radiologists in critical decision‐making processes such as surgical planning, radiotherapy guidance, and follow‐up assessment. This study establishes a foundation for practical deployment in federated clinical environments, particularly within resource‐constrained or privacy‐sensitive institutions.