Blockchain-Enabled Federated Learning for Secure Rural Health Data Sharing

Rural healthcare faces critical challenges such as data privacy, weak digital infrastructure, and limited access to centralized systems. Integrating Blockchain and Federated Learning (FL) offers a transformative framework for secure, decentralized, and privacy-preserving health data sharing. Blockchain ensures data integrity, transparency, and trust through immutable ledgers and smart contracts, while FL enables distributed model training without exposing raw patient data. Together, they create an auditable and collaborative ecosystem for predictive health analytics, disease monitoring, and early detection in rural regions. The proposed Blockchain-FL model supports lightweight consensus, encryption techniques, and energy-efficient protocols suitable for low-resource environments. This approach aligns with ethical AI and global health standards, empowering rural communities with digital trust, sustainable intelligence, and inclusive healthcare transformation.