## 1 Introduction
By surveying the state-of-the-art research and real-world use cases, this review seeks to shed light on the challenges and opportunities that lie in the intersection of formal methods and smart contract development. The foremost concern related to smart contracts is their vulnerability to security breaches and exploitation. Coding errors, such as reentrancy attacks and integer overflows, have led to high-profile hacks, resulting in significant financial losses. The review will address how formal methods can address these security challenges by mathematically proving the correctness of smart contract code, identifying potential vulnerabilities, and ensuring robust security [2]. One of the issues related to the application of formal methods is the choice of suitable specification languages for smart contracts. Different formal languages and tools exist, each with its strengths and limitations. The review will explore the various formal specification languages and their compatibility with smart contract development, considering factors such as expressiveness, usability, and support for automated verification. Smart contracts can be complex and interact with other contracts and external data sources, making their verification challenging [3]. The review will discuss the scalability limitations of formal methods concerning the verification of large-scale smart contracts and potential approaches to mitigate these challenges. The field of smart contracts is relatively new and rapidly evolving. As best practices and standards emerge, the review will examine how formal methods can adapt to evolving development paradigms and assist in establishing guidelines for secure smart contract coding. For formal methods to gain widespread adoption in smart contract development, user-friendly tooling and interfaces are crucial. The review will discuss the accessibility of formal verification tools, the learning curve for developers, and the efforts to democratize the use of formal methods in the industry. Formal verification can significantly enhance security, but it may also introduce overhead that impacts the performance of smart contracts. The review will delve into the trade-offs between security assurances gained through formal methods and the potential impact on contract execution times and gas costs. In today's digitally connected world, data sharing plays a crucial role in various domains, including business, healthcare, research, and government. However, the rise in data breaches, cyberattacks, and legal/regulatory hurdles has created concerns and challenges for organizations and individuals alike. To address these issues effectively, it is essential to gather comprehensive and up-to-date data on the frequency and impact of these incidents. This article delves into the reasons why incorporating more data on breaches, cyberattacks, and legal/regulatory hurdles is vital and how it can pave the way for better data sharing practices [4]. Blockchain technology provides a decentralized and transparent platform for securely managing and sharing data. It offers immutability, traceability, and tamper-proof characteristics, making it an ideal solution for maintaining the integrity of sensitive medical data [5]. Federated learning, on the other hand, enables collaborative model training across distributed data sources, such as EMRs held by various healthcare institutions, without the need for data sharing. The proposed framework establishes a decentralized network where healthcare providers can contribute their locally stored EMR data to collectively train machine learning models. By utilizing federated learning techniques, the models are trained on the distributed data while preserving patient privacy. The blockchain infrastructure ensures transparency and traceability of the learning process, enabling stakeholders to verify the integrity of the models and the data used for training.