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.

本研究提出了一种基于微服务的物联网数据分析系统的方法，通过将实体组织成细粒度、松散连接和可重用的集合，满足隐私和性能要求。我们的方法依赖于联邦学习，可以提高疾病诊断的准确性同时保护数据隐私。此外，我们采用迁移学习来获得更高效的模型。通过使用超过 5800 个公开可用数据集中的肺部 X 光图像进行肺炎检测的实验，我们评估了我们方法的有效性。实验证明，相较于其他先进技术，我们的方法在肺炎识别上表现更好，展示了我们方法优秀的潜在检测性能。

革新疾病诊断：使用联邦学习的基于微服务架构的隐私保护和高效物联网数据分析

Revolutionizing Disease Diagnosis: A Microservices-Based Architecture  for Privacy-Preserving and Efficient IoT Data Analytics Using Federated  Learning