Personalized Healthcare Empowered: Federated Learning Integration with Wearable Device Data for Enhanced Patient Insights

The capacity of wearable technology to monitor health data, send alerts, improve communication, and allow easy access to information while on the road has made it popular in the healthcare industry. Sensors on wearable technology are used to assess physiological characteristics, monitoring which provides a viable way to evaluate individual healthcare. Although there are already a lot of downstream processing methods to give health interventions and detect anomalies, wearable device data contains sensitive health information so user privacy is a problem here. Federated learning addresses this by preserving user privacy while providing accurate insights by maintaining data decentral-ization, enabling model training without sharing raw data. In this paper, we present a framework based on federated learning for wearable-enabled personalized healthcare where we address its guiding principles, obstacles, and possibilities, showcasing its adaptability and potential to enhance patient insights and health care systems.