Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance
Abstract
Malaria surveillance in West Africa faces two conflicting imperatives: Regional collaboration for effective cross-border disease tracking 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.
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