A Survey Paper on Enhancing Privacy and Security in Decentralized Healthcare with Federated Learning and Blockchain

This paper explores how combining these two technologies can enhance privacy and security in decentralized healthcare environments. By integrating Federated Learning and Blockchain, it is possible to create a system where data privacy is maintained, patient consent is enforced, and the integrity of medical records is assured. This approach promises to empower healthcare providers and patients alike, enabling more efficient, secure, and trustworthy healthcare systems. The goal of this research is to analyze the potential benefits and challenges of employing Federated Learning and Blockchain in decentralized healthcare, providing insights into how these technologies can reshape the future of medical data management, privacy, and security. In this context, two emerging technologies — Federated Learning (FL) and Blockchain — offer promising solutions to address these challenges. Federated Learning is a machine learning approach that enables collaborative model training without the need for centralized data sharing. This decentralization of data processing not only preserves patient privacy but also enables more robust and personalized healthcare models. On the other hand, Blockchain, with its immutable and transparent ledger system, ensures secure and traceable transactions of medical records, preventing unauthorized access and tampering.