This paper explores the integration of Federated Learning (FL) and blockchain for decentralized data-sharing in healthcare, aiming to balance data utility and privacy. FL enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data, while blockchain provides a transparent and immutable ledger for secure data transactions. The combination of FL and blockchain creates a secure, private, and efficient data-sharing ecosystem, preserving patient privacy while allowing healthcare providers and researchers to access diverse datasets. The synergy of these technologies enhances data integrity, transparency, and trust in healthcare data-sharing, leading to more accurate models and improved diagnoses. The paper also discusses the challenges and limitations of decentralized data-sharing, including scalability, interoperability, and regulatory compliance. The proposed approach addresses these challenges by leveraging the strengths of FL and blockchain, ensuring secure and efficient data-sharing in healthcare. The paper presents a healthcare use case demonstrating the practical applicability of the framework, leading to more accurate medical models, personalized treatment options, and improved patient care. The integration of FL and blockchain in healthcare data-sharing has the potential to revolutionize the field, fostering advancements in medical research and ensuring regulatory compliance. The paper highlights the benefits of decentralized data-sharing, including enhanced security, privacy, interoperability, and transparency, while also addressing the challenges associated with the technologies. The proposed framework offers a comprehensive solution for decentralized data-sharing in healthcare, setting a new standard for secure and efficient data-sharing. The integration of FL and blockchain in healthcare data-sharing is a promising approach that addresses the critical balance between collaborative healthcare research and patient data privacy, aligning with stringent regulatory standards and enhancing transparency and trust in healthcare data-sharing.This paper explores the integration of Federated Learning (FL) and blockchain for decentralized data-sharing in healthcare, aiming to balance data utility and privacy. FL enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data, while blockchain provides a transparent and immutable ledger for secure data transactions. The combination of FL and blockchain creates a secure, private, and efficient data-sharing ecosystem, preserving patient privacy while allowing healthcare providers and researchers to access diverse datasets. The synergy of these technologies enhances data integrity, transparency, and trust in healthcare data-sharing, leading to more accurate models and improved diagnoses. The paper also discusses the challenges and limitations of decentralized data-sharing, including scalability, interoperability, and regulatory compliance. The proposed approach addresses these challenges by leveraging the strengths of FL and blockchain, ensuring secure and efficient data-sharing in healthcare. The paper presents a healthcare use case demonstrating the practical applicability of the framework, leading to more accurate medical models, personalized treatment options, and improved patient care. The integration of FL and blockchain in healthcare data-sharing has the potential to revolutionize the field, fostering advancements in medical research and ensuring regulatory compliance. The paper highlights the benefits of decentralized data-sharing, including enhanced security, privacy, interoperability, and transparency, while also addressing the challenges associated with the technologies. The proposed framework offers a comprehensive solution for decentralized data-sharing in healthcare, setting a new standard for secure and efficient data-sharing. The integration of FL and blockchain in healthcare data-sharing is a promising approach that addresses the critical balance between collaborative healthcare research and patient data privacy, aligning with stringent regulatory standards and enhancing transparency and trust in healthcare data-sharing.