Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.

翻译：数据编织是一种自动化且由人工智能驱动的数据融合方法，旨在实现数据管理统一化，无需将数据迁移至集中位置即可解决复杂数据问题。在联邦学习架构中，全局模型基于多个本地模型的学习参数进行训练，从而消除了为机器学习而将数据移动至中央存储库的必要性。本文提出了一种基于分布式数据编织架构的安全方法，通过联邦学习与部分同态加密实现医学图像分析。采用该方法，多方无需交换原始数据，而仅需利用学习或融合的特征即可协作训练机器学习模型。该方案符合HIPAA和GDPR等法律法规，确保数据的隐私与安全性。研究通过垂体瘤分类案例验证了该方法的有效性，并取得了显著的准确率。不过，本研究的核心聚焦于联邦学习与部分同态加密作为安全医学图像分析工具的研发与评估。实验结果凸显了这些技术在其他隐私敏感领域的应用潜力，并为安全与隐私保护的机器学习研究体系贡献了增量价值。