Deep Federated Learning for IoT-based Decentralized Healthcare Systems

Abstract

Recent trends in the healthcare industry, such as the use of wearable IoT for continuous health monitoring, are setting new requirements for healthcare systems that boost data analysis. These systems should support decentralization and maintain the privacy and ownership of users' data due to the sensitivity of healthcare data. Therefore, the use of federated learning techniques is recommended for systems that need such requirements. This paper proposes a Deep Federated Learning framework for decentralized healthcare systems that maintain user privacy in a distributed architecture. It also proposes an algorithm for an automated training data acquiring process. Furthermore, it presents an experiment for using deep federated learning in detecting skin diseases and using Transfer Learning to address the problem of limited availability of healthcare data in building deep learning models. The evaluated results show how the federated learning increased the Area Under the Curve of the centralized learning model up to 0.97, as it also shows good model performance during federated rounds in terms of accuracy, precision, recall, and F1-score. Moreover, although the FL system has affected the quality of service to the user in terms of model conversion time, the Federated Learning system meets the requirements of building models in a decentralized manner with no sharing of users' private data.