Privacy preserving loneliness detection: A federated learning approach

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dc.contributor.authorQirtas, Malik Muhammaden
dc.contributor.authorPesch, Dirken
dc.contributor.authorZafeiridi, Evien
dc.contributor.authorBantry White, Eleanoren
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-03-31T12:37:20Z
dc.date.available2023-03-31T12:37:20Z
dc.date.issued2022-08-24en
dc.description.abstractToday's smartphones have sensors that enable monitoring and collecting data on users' daily activities, which may be converted into behavioral indicators of users' health and well-being. Although previous research has used passively sensed data through smartphones to identify users' mental health state, including loneliness, anxiety, depression, and even schizophrenia, the issue of user data privacy in this context has not been well addressed. Here we focus on the feeling of loneliness, which, if persistent, is associated with a number of negative health outcomes. While modern artificial intelligence technology, specifically machine learning, can assist in detecting loneliness or depression, current approaches have applied machine learning to centrally collected user data at a single location with the potential to compromise user data privacy. To address the issue of privacy, we investigated the feasibility of using federated learning on single user data to identify loneliness collected by different smartphone sensors. Federated learning can help protect user privacy by avoiding the transmission of sensitive data from mobile devices to a central server location. To evaluate the federated method's performance in detecting loneliness, we also trained models on all user data using a centralised machine learning approach and compared the results. The results indicate that federated learning has considerable promise for detecting loneliness in a binary classification problem while maintaining user data privacy.en
dc.description.sponsorshipScience Foundation Ireland (SFI Centre for Research Training in Advanced Networks for Sustainable Societies (ADVANCE CRT))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQirtas, M. M., Pesch, D., Zafeiridi, E. and White, E. B. (2022) 'Privacy preserving loneliness detection: A federated learning approach', 2022 IEEE International Conference on Digital Health (ICDH), Barcelona, Spain, 10-16 July, pp. 157-162. doi: 10.1109/ICDH55609.2022.00032en
dc.identifier.doi10.1109/icdh55609.2022.00032en
dc.identifier.endpage162en
dc.identifier.isbn978-1-6654-8149-6en
dc.identifier.isbn978-1-6654-8150-2en
dc.identifier.startpage157en
dc.identifier.urihttps://hdl.handle.net/10468/14346
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2022 IEEE International Conference on Digital Health (ICDH)en
dc.rights© 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectmHealthen
dc.subjectSensingen
dc.subjectWearablesen
dc.subjectPrivacyen
dc.subjectLonelinessen
dc.subjectFederated learningen
dc.titlePrivacy preserving loneliness detection: A federated learning approachen
dc.typeConference itemen
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