## 2 Motivation  
The motivation behind exploring blockchain-enabled federated learning with electronic medical records (EMRs) in precision medicine is driven by the pressing need to overcome challenges and limitations associated with traditional healthcare data sharing and analysis. The field of precision medicine holds immense promise in revolutionizing healthcare by providing personalized treatments and therapies. However, realizing the full potential of precision medicine requires access to comprehensive and diverse medical data from various sources. Data privacy and security concerns have posed significant obstacles to sharing sensitive healthcare data. Patient confidentiality, legal regulations, and the risk of data breaches have restricted the availability of data for research and analysis purposes [16]. Furthermore, the centralization of data in healthcare systems raises concerns about data ownership, control, and biases that may hinder the development of robust and unbiased models. Blockchain technology offers a compelling solution to address these challenges. Its decentralized and immutable nature, coupled with cryptographic protocols, provides a secure and transparent platform for data sharing and analysis. By applying blockchain-enabled federated learning to EMRs, healthcare institutions and researchers can collaboratively train machine learning models without compromising patient privacy. This distributed approach allows for the extraction of valuable insights from diverse datasets while keeping the data localized, reducing the risks associated with data centralization [17]. The potential impact of unlocking revolutionary insights in precision medicine through blockchain-enabled federated learning is vast. Enhanced diagnostic accuracy, optimized treatment plans, the identification of specific patient subpopulations for clinical trials, and the accelerated development of novel therapies are among the envisioned benefits. By leveraging the power of collective intelligence and collaborative efforts, healthcare professionals and researchers can navigate complex medical challenges and make significant advancements in patient care. The motivation to explore this innovative approach is also fueled by the need for trust and transparency in healthcare systems [18]. Blockchain technology provides an auditable and tamper-proof record of data transactions, fostering trust among stakeholders. By addressing concerns related to data integrity, consent management, and traceability, blockchain-enabled federated learning can promote greater collaboration, data sharing, and knowledge exchange among healthcare institutions, researchers, and patients. In conclusion, the motivation behind investigating blockchain-enabled federated learning with EMRs in precision medicine stems from the urgent need to overcome the limitations of traditional data sharing and analysis methods in healthcare. By leveraging blockchain's decentralized and secure framework, healthcare professionals can unlock revolutionary insights while protecting patient privacy. The potential benefits in precision medicine are substantial, with the ultimate goal of providing personalized and improved healthcare outcomes for individuals [19]. The motivation behind this research stems from the pressing need to unlock revolutionary medical insights while addressing critical challenges in the healthcare domain. The convergence of blockchain technology and federated learning with electronic medical records (EMRs) presents a promising opportunity to address these challenges and revolutionize healthcare analytics. The following factors serve as strong motivators for pursuing this research:Privacy Preservation: EMRs contain sensitive patient information that must be protected to comply with privacy regulations and maintain patient trust. The motivation to develop a framework that ensures privacy preservation while enabling collaborative analysis of EMRs arises from the need to strike a balance between data sharing for research purposes and safeguarding patient privacy [20].