A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring

Mobile technologies and AI enable health data collection from devices, allowing effective monitoring. Traditional methods often compromise privacy, but federated learning (FL) offers a potential solution. However, current FL approaches face two issues: they don’t identify key health features for clinical intervention, and their aggregation overlooks patient multidimensional heterogeneity. This study seeks to develop a new FL method to tackle these challenges and enhance privacy in mobile health monitoring. This study proposes a novel FL method combining (1) a spatial and temporal attention-based prediction model (STA-Pred) that uses attention to identify key spatial and temporal features, and (2) a multidimensional heterogeneity-based aggregation protocol (MDH-Aggr), which aggregates components based on their heterogeneity to handle multidimensional differences. Experiments on three data sets show that our method outperforms existing methods in several patient-monitoring contexts. This study enhances understanding of how to leverage mobile technologies and AI to enable privacy-preserving health monitoring that promotes the social good. Additionally, it advances FL research through two innovative designs (STA-Pred and MDH-Aggr).

History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good.

Funding: Y. Chai, H. Liu, and Y. Liu are supported by the National Natural Science Foundation of China [Grants 72342011, 72322019, 72188101, and 72402001]. Dr. L. Wang’s work was in part supported by the National Natural Science Foundation of China [Grant 72271027], Hainan Provincial Natural Science Foundation of China [Grant 726MS0458], and Beijing Institute of Technology Research Fund Program for Young Scholars [Grant XSQD-202216004]. X. Liu is not supported by any funds or associated with any of the abovementioned funds.

Supplemental Materials: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0521) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0521). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.