Federated Learning for Healthcare Challenge

African healthcare systems generate valuable data that could train life-saving AI models, but this data remains siloed in individual institutions due to privacy concerns, regulatory restrictions, and lack of data-sharing infrastructure. Small clinics cannot develop AI tools with their limited datasets, while larger institutions cannot legally share patient information for collaborative research. This fragmentation prevents African healthcare from benefiting from AI advances that require large, diverse datasets. Developers must create federated learning systems that enable multiple clinics to collaboratively train models on conditions like diabetes, hypertension, and maternal health without exposing patient data, working within bandwidth constraints and regulatory requirements while delivering models that improve diagnostic accuracy and patient outcomes.

The Federated Learning for Healthcare Challenge seeks innovative implementations of privacy-preserving machine learning that enable healthcare institutions to collaborate on AI model development without sharing sensitive patient data. This challenge addresses the critical need for AI in healthcare while respecting patient privacy and data sovereignty requirements.

Participants will develop federated learning systems that can train machine learning models across multiple healthcare facilities for common conditions like diabetes, hypertension, maternal health complications, tuberculosis, and malaria. The system must enable clinics to benefit from collective learning while keeping all patient data local, addressing both technical and regulatory challenges of health data sharing.

Successful solutions will implement secure aggregation protocols, handle heterogeneous data distributions across clinics, work with limited bandwidth connections, and provide differential privacy guarantees. The system should support various model types including diagnostic classifiers, risk prediction models, and treatment recommendation systems while maintaining model performance comparable to centralized training.

We particularly value solutions that work with basic infrastructure found in African clinics, provide clear audit trails for regulatory compliance, include bias detection and mitigation strategies, and offer intuitive interfaces for non-technical healthcare workers. The platform should demonstrate clear improvements in diagnostic accuracy or patient outcomes while maintaining strict privacy standards.