Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case

In the era of data-driven healthcare, the amalgamation of blockchain and federated learning (FL) introduces a paradigm shift toward secure, collaborative, and patient-centric data sharing. This article pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. This article elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data sharing. This article vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified data sets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.