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Abstract

Background

The 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.

Aim

Our aim was to understand the potential data protection implications of the use of federated learning for public health purposes.

Methods

Building upon semi-structured interviews (n = 14) and a panel discussion (n = 5) with key opinion leaders in Europe, including both FL and GDPR experts, we explored how GDPR principles would apply to the implementation of FL within public health.

Results

Whereas this study found that FL offers substantial benefits such as data minimisation, storage limitation and effective mitigation of many of the privacy risks of sharing personal data, it also identified various challenges. These challenges mostly relate to the increased difficulty of checking data at the source and the limited understanding of potential adverse outcomes of the technology.

Conclusion

Since FL is still in its early phase and under rapid development, it is expected that knowledge on its impracticalities will increase rapidly, potentially addressing remaining challenges. In the meantime, this study reflects on the potential of FL to align with data protection objectives and offers guidance on GDPR compliance.

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2024-09-19
2024-12-24
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The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Natalie+Lieftink&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Lieftink Natalie</a>, <a href="/search?value1=Carolina+dos+S+Ribeiro&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Ribeiro Carolina dos S</a>, <a href="/search?value1=Mark+Kroon&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Kroon Mark</a>, <a href="/search?value1=George+B+Haringhuizen&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Haringhuizen George B</a>, <a href="/search?value1=Albert+Wong&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wong Albert</a>, <a href="/search?value1=Linda+HM+van+de+Burgwal&option1=author&noRedirect=true" class="nonDisambigAuthorLink">van de Burgwal Linda HM</a></span>. The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021. <a href="/content/ecdc">Euro Surveill.</a> 2024;29(38):pii=2300695. <a href="https://doi.org/10.2807/1560-7917.ES.2024.29.38.2300695" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2024.29.38.2300695</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 03 Dec 2023;&nbsp;&nbsp; <span class="generated">Accepted</span>: 18 Jun 2024 </span> </p>
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