FedSegNet: A Federated Learning Framework for 3D Medical Image Segmentation

Medical image segmentation plays a vital role in diagnostic imaging and treatment planning, especially for volumetric modalities such as MRI and CT scans. However, training high-performance deep learning models for 3D medical image segmentation requires large, annotated datasets, which are often siloed due to strict privacy laws like HIPAA and GDPR. Federated Learning (FL) offers a decentralized solution that enables collaborative training without sharing raw patient data, but its effectiveness is hindered by challenges such as data heterogeneity, communication overhead, and model degradation on non-IID datasets. In this study, we propose FedSegNet, a novel federated learning framework tailored for 3D medical image segmentation. FedSegNet integrates a Transformer-based U-Net architecture for capturing both local and global spatial features and introduces an Adaptive Aggregation Mechanism (AAM) to dynamically weigh client updates based on data quality, performance, and divergence. To reduce communication costs, the framework employs gradient sparsification and quantization techniques. We evaluate FedSegNet on multi-institutional datasets including BraTS, LiTS, and ACDC, using metrics such as Dice Similarity Coefficient and Hausdorff Distance. Results show that FedSegNet achieves up to 7.2% improvement in segmentation accuracy and 38% reduction in communication cost compared to existing methods, demonstrating its potential for secure, decentralized medical AI applications.