## 1 Introduction  
Precision medicine, an innovative approach to healthcare, aims to deliver personalized treatments and therapies tailored to the unique characteristics of each patient. The realization of precision medicine heavily relies on the availability of comprehensive and diverse medical data. However, traditional approaches to data sharing and analysis in healthcare face numerous challenges, particularly regarding data privacy and security. In recent years, blockchain technology has emerged as a promising solution to address these challenges and unlock revolutionary insights in precision medicine. This introduction explores the potential of blockchain-enabled federated learning in conjunction with electronic medical records (EMRs) to empower precision medicine. By leveraging the inherent features of blockchain, such as immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby overcoming critical barriers to data sharing and analysis [1]. Federated learning, as a decentralized machine learning paradigm, enables healthcare institutions and research organizations to collaboratively train models on locally stored EMR data, eliminating the need for centralized data storage. The blockchain serves as a decentralized ledger, securely recording the training process and aggregating model updates while preserving the privacy of sensitive patient information. This approach facilitates the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and diverse patient populations. The potential impact of unlocking revolutionary insights through blockchain-enabled federated learning and EMRs in precision medicine is substantial. Envisioned benefits include an improved accuracy in diagnosis, the optimization of treatment plans, the identification of suitable subpopulations for clinical trials, and the accelerated development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in advancing precision medicine. In light of these advancements, this paper aims to explore the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By enabling secure and decentralized data sharing and analysis, this paradigm shift can revolutionize healthcare delivery, ultimately leading to more personalized, efficient, and tailored care for each patient. The subsequent sections will delve into the intricacies of blockchain technology, federated learning, and their synergistic application in precision medicine, shedding light on the benefits, challenges, and future prospects of this innovative approach [1]. Smart contracts have emerged as a transformative technology within the realm of blockchain and decentralized applications. These self-executing contracts, written in code and residing on a blockchain, facilitate automated and trustless transactions, eliminating the need for intermediaries. Though the promise of smart contracts is enticing, their widespread adoption has also raised concerns about potential vulnerabilities and bugs that could lead to financial losses and security breaches. To ensure the reliability and safety of smart contracts, formal methods have gained attention as a means of verification and validation. The aim of the formal methods for smart contracts was to explore the application of formal methods in the context of smart contract development and deployment. This provides a comprehensive analysis of various formal verification techniques, their effectiveness, and their limitations in ensuring the correctness and security of smart contracts.