Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking
In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA,...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Sensor and Actuator Networks |
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| Online Access: | https://www.mdpi.com/2224-2708/14/1/11 |
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| author | Nikolaos Pavlidis Andreas Sendros Theodoros Tsiolakis Periklis Kostamis Christos Karasoulas Eleni Briola Christos Chrysanthos Nikolaidis Vasilis Perifanis George Drosatos Eleftheria Katsiri Despoina Elisavet Filippidou Anastasios Manos Pavlos S. Efraimidis |
| author_facet | Nikolaos Pavlidis Andreas Sendros Theodoros Tsiolakis Periklis Kostamis Christos Karasoulas Eleni Briola Christos Chrysanthos Nikolaidis Vasilis Perifanis George Drosatos Eleftheria Katsiri Despoina Elisavet Filippidou Anastasios Manos Pavlos S. Efraimidis |
| author_sort | Nikolaos Pavlidis |
| collection | DOAJ |
| description | In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. In addition, FLORA’s design incentivizes participation through a token-based reward system, fostering collaborative data contributions. This work illustrates how the integration of cutting-edge technologies creates a secure, scalable, and user-centric health application, setting a new standard for privacy-preserving digital health platforms. |
| format | Article |
| id | doaj-art-00767e5e3a3d48438b00cf67fc2e3077 |
| institution | DOAJ |
| issn | 2224-2708 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj-art-00767e5e3a3d48438b00cf67fc2e30772025-08-20T02:44:50ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082025-01-011411110.3390/jsan14010011Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation TrackingNikolaos Pavlidis0Andreas Sendros1Theodoros Tsiolakis2Periklis Kostamis3Christos Karasoulas4Eleni Briola5Christos Chrysanthos Nikolaidis6Vasilis Perifanis7George Drosatos8Eleftheria Katsiri9Despoina Elisavet Filippidou10Anastasios Manos11Pavlos S. Efraimidis12Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceOPSIS Research, Strada Corbita 30, Parter, Sector 5, 51083 Bucharest, RomaniaOPSIS Research, Strada Corbita 30, Parter, Sector 5, 51083 Bucharest, RomaniaInstitute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, GreeceIn an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. In addition, FLORA’s design incentivizes participation through a token-based reward system, fostering collaborative data contributions. This work illustrates how the integration of cutting-edge technologies creates a secure, scalable, and user-centric health application, setting a new standard for privacy-preserving digital health platforms.https://www.mdpi.com/2224-2708/14/1/11federated learningblockchainprivacymachine learningencryption |
| spellingShingle | Nikolaos Pavlidis Andreas Sendros Theodoros Tsiolakis Periklis Kostamis Christos Karasoulas Eleni Briola Christos Chrysanthos Nikolaidis Vasilis Perifanis George Drosatos Eleftheria Katsiri Despoina Elisavet Filippidou Anastasios Manos Pavlos S. Efraimidis Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking Journal of Sensor and Actuator Networks federated learning blockchain privacy machine learning encryption |
| title | Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking |
| title_full | Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking |
| title_fullStr | Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking |
| title_full_unstemmed | Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking |
| title_short | Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking |
| title_sort | federated learning for privacy friendly health apps a case study on ovulation tracking |
| topic | federated learning blockchain privacy machine learning encryption |
| url | https://www.mdpi.com/2224-2708/14/1/11 |
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