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.

本文介绍了一种使用联邦学习和部分同态加密在分布式数据结构架构中进行安全医学图像分析的方法，此方法允许多方合作训练机器学习模型而无须交换原始数据，以符合HIPAA和GDPR等法律法规，确保数据的隐私和安全。研究通过垂体肿瘤分类的案例研究验证了该方法的有效性，并强调了联邦学习和部分同态加密在安全医学图像分析中的潜力，为安全和隐私保护机器学习领域的不断发展提供了贡献。

一种利用同态加密和联邦学习的先进数据布局体系结构