Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers).

翻译：联邦学习（FL）使得跨机构协作训练模型成为可能，而无需共享敏感数据，这使其成为医学影像任务中极具吸引力的解决方案。然而，由于各机构间成像协议和患者人口统计特征的差异，传统FL方法（如联邦平均算法FedAvg）在跨域泛化方面面临困难。这一挑战在胰腺MRI分割中尤为明显，其中解剖结构变异性和成像伪影会显著影响性能。本文对胰腺MRI分割的FL算法进行了全面评估，并提出了一种引入自适应聚合权重的新方法。通过在模型聚合过程中动态调整每个客户端的贡献，我们的方法能够考虑特定领域的差异，并提升在异构数据集上的泛化能力。实验结果表明，与传统FL方法相比，我们的方法在保持隐私保护能力的同时，提高了分割精度并减少了域偏移的影响。在多家医院（中心）均观察到了显著的性能提升。