## 2 Motivation
2. _Data Fragmentation and Heterogeneity:_ Healthcare data are distributed across various healthcare institutions, leading to data fragmentation and heterogeneity. Leveraging federated learning and blockchain empowers healthcare organizations to collaborate and train machine learning models on this distributed data, allowing for comprehensive insights while preserving data sovereignty [14]. 3. _Ethical and Legal Concerns:_ The ethical and legal considerations surrounding the use of EMRs necessitate the development of robust frameworks. By incorporating transparency, traceability, and privacy-enhancing technologies, the proposed framework aims to address these concerns and ensure compliance with regulations, thereby fostering trust among patients, healthcare providers, and researchers [21]. 4. _Improved Healthcare Decision-Making:_ The ability to unlock revolutionary medical insights has the potential to transform healthcare decision-making. By leveraging the collective intelligence of distributed EMR data, the proposed framework can aid in disease prediction, treatment recommendations, precision medicine, and resource allocation, leading to improved patient outcomes and optimized healthcare delivery [16]. 5. _Advancement of Medical Research:_ The integration of blockchain and federated learning opens up new avenues for medical research. The framework can facilitate collaboration among researchers, promote knowledge sharing, and enable large-scale analysis of diverse EMR datasets. This has the potential to accelerate medical discoveries, support evidence-based medicine, and drive innovation in healthcare [17]. 6. _Scalability and Real-World Applicability:_ Scalability is a critical factor in the success of any framework deployed in healthcare settings. The motivation to develop a scalable framework arises from the need to accommodate a growing number of healthcare institutions, handle diverse datasets, and ensure real-world applicability across different healthcare environments [22]. The motivation behind this research lies in addressing the challenges and harnessing the opportunities presented by the convergence of blockchain and federated learning in the context of EMRs. By developing a robust framework, this research aims to unlock revolutionary medical insights, enhance privacy preservation, foster collaboration, and drive advancements in healthcare analytics and decision-making [19]. Blockchain-enabled federated learning with EMRs (electronic medical records) is a promising approach that combines the benefits of blockchain technology and federated learning to address the challenges of traditional healthcare data sharing and analysis. Although the Motivation Section provides an overview of the reasons behind its promise, further elaboration and clarification can strengthen the understanding of its potential impact. Let us delve deeper into the key aspects that warrant more explanation:  
1. Blockchain's Role in Data Security: Blockchain is a distributed and immutable ledger that stores data in blocks, cryptographically linked together in a chain. Each block contains a unique hash of the previous block, ensuring the integrity of the data. Additionally, consensus mechanisms used in blockchain prevent unauthorized changes to the data. By employing blockchain, patient data can be stored in a decentralized and tamper-resistant manner, reducing the risk of data breaches and enhancing patient privacy [23]. 2. Federated Learning for Collaborative Research: Federated learning enables model training on decentralized data sources without sharing raw data. Instead of centralizing all data in one location, the model is sent to local data sources (such as hospitals) where it learns from the data and then sends back only the model updates. This approach ensures data privacy and compliance with regulations while enablingcollaborative research across multiple institutions. Expanding on this concept can emphasize the importance of preserving data privacy while facilitating knowledge sharing [17]. 3.