Revolutionizing Disease Diagnosis: A Microservices-Based Architecture
  for Privacy-Preserving and Efficient IoT Data Analytics Using Federated
  Learning
Safa Ben Atitallah Maha Driss Henda Ben Ghezala
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.