Exploring Perceptions of Federated Learning in Self-Tracking Apps: A Qualitative Study with Mostly Female University Students

Self-tracking apps are a prominent component of today’s wellness and health management strategies. These apps collect a variety of personal data which is typically stored in a remote central location and utilised by machine learning models. Federated Learning (FL) brings about a fundamental change in this for machine learning. Through interviews and one workshop with 18 university students (majority of whom (16) identified as women), we investigate whether users’ increased awareness of enhanced-privacy protection in exchange for potentially reduced accuracy and fairness afforded by FL can alter users’ perceptions of these apps and the types of data they feel comfortable sharing. Participants’ willingness to pay for the FL application varied depending on their sensitivity towards data privacy. However, participants were still reluctant to share certain types of data even after knowing that FL keeps data on their devices. This was due to a persistent lack of trust in companies accompanied by a lack of awareness of the privacy implications of sharing sensitive data. Our findings suggest the need to further inform and educate users about the privacy advantages of FL. Companies must also prioritise establishing trust with users, as this was found to be a strong factor in increasing users’ acceptability of the apps, irrespective of the privacy-enhanced technology employed.