革新疾病诊断：一种基于微服务的、采用联邦学习的隐私保护型高效物联网数据分析架构
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning
- University of Manouba（马努巴大学）
- Prince Sultan University（苏丹王子大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对传统中心化疾病诊断系统的隐私问题，提出基于微服务的物联网数据分析架构，结合联邦学习与迁移学习，经5800余张胸部X光图像验证，肺炎检测性能优于前沿技术。
AI中文摘要：
基于深度学习的疾病诊断应用对于在各疾病阶段实现精准诊断至关重要。然而，传统中心化学习系统因使用个人数据而面临隐私隐患。另一方面，分布式计算范式通过将处理资源部署得更靠近设备，并支持更高效的数据分析，有望革新疾病诊断模式。在医疗领域，数据分析结果需具备低延迟、高可靠性与高可信度，因此可扩展的数据分析架构同样至关重要。本研究提出一种面向物联网数据分析系统的微服务方案，通过将各类实体组织为细粒度、松耦合且可复用的集合，以满足隐私与性能需求。该方案依托federated learning（联邦学习），可在保护数据隐私的同时提升疾病诊断准确率。此外，我们采用transfer learning（迁移学习）以获得更高效的模型。我们使用公开数据集中超5800张用于肺炎检测的胸部X光图像开展实验，以评估该方案的有效性。实验结果表明，该方案在肺炎识别方面的表现优于其他前沿技术，展现出极具潜力的检测性能。
英文摘要：
Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer to the device and enabling more effective data analyses, a distributed computing paradigm has the potential to revolutionize disease diagnosis. Scalable architectures for data analytics are also crucial in healthcare, where data analytics results must have low latency and high dependability and reliability. This study proposes a microservices-based approach for IoT data analytics systems to satisfy privacy and performance requirements by arranging entities into fine-grained, loosely connected, and reusable collections. Our approach relies on federated learning, which can increase disease diagnosis accuracy while protecting data privacy. Additionally, we employ transfer learning to obtain more efficient models. Using more than 5800 chest X-ray images for pneumonia detection from a publicly available dataset, we ran experiments to assess the effectiveness of our approach. Our experiments reveal that our approach performs better in identifying pneumonia than other cutting-edge technologies, demonstrating our approach's promising potential detection performance.
