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Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance

Domain:

healthcare

Record type:

paperdataset
Creator:
AgyOpo
Publisher:
Zenodo
Host:avatar

Malaria surveillance in West Africa faces two conflicting imperatives:

  1. Regional collaboration for effective cross-border disease tracking
  2. Data sovereignty mandated by national data protection regulations

This work demonstrates that federated learning (FL) can enable privacy-preserving collaboration while handling extreme data heterogeneity across countries. We compare 8 methods including centralized baselines and 6 federated learning algorithms on real malaria surveillance data from Ghana, Mali, Nigeria, and Burkina Faso.

Visit

doi.org

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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