Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect 5 real medical datasets, including 2 public medical image datasets and 3 private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.

翻译：尽管数据驱动方法在疾病诊断和治疗中通常表现显著，但其因收集数据进行模型训练而存在隐私泄露的隐患。近年来，联邦学习提供了一种安全可信的替代方案，能够在多个机构间无需交换医疗数据的情况下协作训练模型。因此，因其在隐私保护方面的天然优势而备受关注。然而，当不同医院间存在异构医疗数据时，联邦学习通常面临性能下降的问题。本文提出了一种新的联邦学习个性化框架以解决该问题。该框架基于局部数据相似性感知成功生成个性化模型，并在泛化性与个性化之间实现了比现有方法更优的权衡。随后，我们进一步设计了一种差分稀疏正则化器，以提升模型训练过程中的通信效率。此外，我们提出了一种有效降低计算开销的方法，显著提高了计算效率。进一步地，我们收集了5个真实医疗数据集，包括2个公开医疗图像数据集和3个私有多中心临床诊断数据集，通过结节分类、肿瘤分割和临床风险预测任务评估了模型性能。在与13种现有相关方法的对比中，所提方法成功实现了最佳模型性能，同时通信效率最高提升60%。源代码已公开，可通过以下链接访问：https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code。