Federated learning: a privacy-preserving approach to data-centric regulatory cooperation
Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Drug Safety and Regulation |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdsfr.2025.1579922/full |
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| author | Alexander Horst Paul Loustalot Sanjeev Yoganathan Ting Li Joshua Xu Weida Tong David Schneider Nicolas Löffler-Perez Erminio Di Renzo Michael Renaudin |
| author_facet | Alexander Horst Paul Loustalot Sanjeev Yoganathan Ting Li Joshua Xu Weida Tong David Schneider Nicolas Löffler-Perez Erminio Di Renzo Michael Renaudin |
| author_sort | Alexander Horst |
| collection | DOAJ |
| description | Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by enabling collaborative training of machine learning models without the need for direct data sharing, thereby preserving privacy and overcoming legal hurdles. We illustrate how Swissmedic, the Swiss Agency for Therapeutic Products, together with its partner agencies, proposes to use federated learning to improve TRICIA, an AI tool for assessing incoming reports of serious incidents related to medical devices. This approach enables the development of robust, generalisable risk assessment models that can potentially improve current processes. A proof of concept was deployed and thoroughly tested during the 14th Global Summit on Regulatory Science using synthetic data with participants from Swissmedic, the U.S. Food and Drug Administration (FDA), and the Danish Medicines Agency (DKMA), with promising initial results. This innovation has the potential to serve as a roadmap for other regulators to adopt similar approaches to optimize their own regulatory processes, contributing to a more integrated and efficient regulatory environment worldwide. |
| format | Article |
| id | doaj-art-676dae16beda46c4bb07dbad9013c99b |
| institution | OA Journals |
| issn | 2674-0869 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Drug Safety and Regulation |
| spelling | doaj-art-676dae16beda46c4bb07dbad9013c99b2025-08-20T01:56:49ZengFrontiers Media S.A.Frontiers in Drug Safety and Regulation2674-08692025-05-01510.3389/fdsfr.2025.15799221579922Federated learning: a privacy-preserving approach to data-centric regulatory cooperationAlexander Horst0Paul Loustalot1Sanjeev Yoganathan2Ting Li3Joshua Xu4Weida Tong5David Schneider6Nicolas Löffler-Perez7Erminio Di Renzo8Michael Renaudin9Swissmedic 4.0 and Medical Device Vigilance, Swissmedic, Swiss Agency for Therapeutic Products, Bern, SwitzerlandQuinten Health, Paris, FranceDivision of Medical Devices, The Danish Medicines Agency, Copenhagen, DenmarkNational Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United StatesNational Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United StatesNational Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United StatesSwissmedic 4.0 and Medical Device Vigilance, Swissmedic, Swiss Agency for Therapeutic Products, Bern, SwitzerlandSwissmedic 4.0 and Medical Device Vigilance, Swissmedic, Swiss Agency for Therapeutic Products, Bern, SwitzerlandSwissmedic 4.0 and Medical Device Vigilance, Swissmedic, Swiss Agency for Therapeutic Products, Bern, SwitzerlandSwissmedic 4.0 and Medical Device Vigilance, Swissmedic, Swiss Agency for Therapeutic Products, Bern, SwitzerlandRegulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by enabling collaborative training of machine learning models without the need for direct data sharing, thereby preserving privacy and overcoming legal hurdles. We illustrate how Swissmedic, the Swiss Agency for Therapeutic Products, together with its partner agencies, proposes to use federated learning to improve TRICIA, an AI tool for assessing incoming reports of serious incidents related to medical devices. This approach enables the development of robust, generalisable risk assessment models that can potentially improve current processes. A proof of concept was deployed and thoroughly tested during the 14th Global Summit on Regulatory Science using synthetic data with participants from Swissmedic, the U.S. Food and Drug Administration (FDA), and the Danish Medicines Agency (DKMA), with promising initial results. This innovation has the potential to serve as a roadmap for other regulators to adopt similar approaches to optimize their own regulatory processes, contributing to a more integrated and efficient regulatory environment worldwide.https://www.frontiersin.org/articles/10.3389/fdsfr.2025.1579922/fullfederated learningregulatory sciencesmedical devicesrisk assessmentswissmedicdata privacy |
| spellingShingle | Alexander Horst Paul Loustalot Sanjeev Yoganathan Ting Li Joshua Xu Weida Tong David Schneider Nicolas Löffler-Perez Erminio Di Renzo Michael Renaudin Federated learning: a privacy-preserving approach to data-centric regulatory cooperation Frontiers in Drug Safety and Regulation federated learning regulatory sciences medical devices risk assessment swissmedic data privacy |
| title | Federated learning: a privacy-preserving approach to data-centric regulatory cooperation |
| title_full | Federated learning: a privacy-preserving approach to data-centric regulatory cooperation |
| title_fullStr | Federated learning: a privacy-preserving approach to data-centric regulatory cooperation |
| title_full_unstemmed | Federated learning: a privacy-preserving approach to data-centric regulatory cooperation |
| title_short | Federated learning: a privacy-preserving approach to data-centric regulatory cooperation |
| title_sort | federated learning a privacy preserving approach to data centric regulatory cooperation |
| topic | federated learning regulatory sciences medical devices risk assessment swissmedic data privacy |
| url | https://www.frontiersin.org/articles/10.3389/fdsfr.2025.1579922/full |
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