The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021.

ABSTRACT: BackgroundThe wide application of machine learning (ML) holds great potential to improve public health by supporting data analysis informing policy and practice. Its application, however, is often hampered by data fragmentation across organisations and strict regulation by the General Data Protection Regulation (GDPR). Federated learning (FL), as a decentralised approach to ML, has received considerable interest as a means to overcome the fragmentation of data, but it is yet unclear to which extent this approach complies with the GDPR.AimOur aim was to understand the potential data protection implications of the use of federated learning for public health purposes.MethodsBuilding upon semi-structured interviews (n = 14) and a panel discussion (n = 5) with key opinion leaders in Europe, inc

SUBMITTER: Lieftink N 

PROVIDER: S-EPMC11484284 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature