## 1 Introduction
Ultimately, the research findings were publicly accessible, bolstering the credibility of the study and its potential impact on precision medicine approaches. By integrating such real-world examples into our research paper, we aim to showcase the transformative potential of our blockchain-enabled federated learning framework in revolutionizing precision medicine. Through safeguating patient privacy, ensuring scalability, and promoting transparency in medical data sharing, our approach addresses critical challenges in healthcare and paves the way for more effective, secure, and collaborative precision medicine practices [4]. Data sharing and analysis in healthcare have been crucial for medical advancements and improving patient outcomes. However, traditional approaches to data sharing and analysis have faced numerous challenges, especially concerning data privacy and security [8]. Let us delve into the details of these challenges and their impact on healthcare delivery:  
1. _Patient Privacy Concerns:_ One of the primary challenges in traditional data sharing is protecting patient privacy. Healthcare data often contain sensitive information, such as medical history, diagnoses, medications, and genetic data. Unauthorized access or data breaches can lead to identity theft, discrimination, and the compromise of personal health information. Patients may be reluctant to share their data if they fear these could be mishandled or misused, which can hinder medical research and collaboration. 2. _Data Silos and Fragmentation:_ Healthcare data are typically stored in various systems, such as electronic health records (EHRs) in hospitals, medical imaging archives, and specialized databases. These data silos hinder data sharing and analysis across different institutions and research centers. Integrating data from diverse sources is complex and time-consuming, limiting the comprehensive view of patient health and hindering the discovery of holistic treatment options. * 3. _Lack of Interoperability:_ Related to data silos is the lack of interoperability between different healthcare systems and software. Different institutions may use disparate data formats and standards, making it difficult to exchange data seamlessly. This lack of interoperability impedes data sharing for research and can delay critical decision-making in patient care [9]. * 4. _Inadequate Data Governance:_ Many healthcare organizations lack a well-defined data governance framework. This absence of clear rules and responsibilities can lead to inconsistent data handling practices, increasing the risk of data breaches and privacy violations. Proper data governance is essential to ensure that data sharing and analysis adhere to legal and ethical standards [6]. * 5. _Security Vulnerabilities:_ Traditional healthcare systems may use outdated security measures, making them vulnerable to cyberattacks. As the healthcare sector becomes more digital, it becomes an attractive target for hackers seeking valuable data. Ransomware attacks on hospitals and healthcare providers have disrupted patient care and raised concerns about the security of medical records [10]. * 6. _Consent and Opt-Out Challenges:_ Obtaining informed consent from patients for data sharing and analysis can be challenging, especially for large-scale research initiatives. Complex consent forms and procedures can confuse patients, leading to suboptimal participation rates. Additionally, the option to opt-out of data sharing can affect the representativeness and completeness of research datasets, limiting the generalizability of findings [11]. * 7. _Regulatory Compliance:_ Healthcare data are subject to strict regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe. Complying with these regulations can be burdensome, particularly when sharing data across international borders. Non-compliance can result in severe penalties, further deterring data sharing efforts [12]. * 8.