# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
## 2 Background
### Blockchain  
Blockchain is a decentralized, distributed digital ledger technology that securely and transparently records transactions. It consists of blocks containing a set of transactions cryptographically linked to the previous block. One of the defining features of blockchain is its immutability, meaning that once a transaction is recorded, it cannot be altered or deleted without consensus from network participants [19]. In federated learning, blockchain enhances transparency, accountability, and data integrity by creating a tamper-proof record of model updates and interactions between nodes [20]. This ensures that all participants can verify the authenticity of updates throughout the training process, fostering trust among decentralized entities.  
A key component of blockchain technology is smart contracts--self-executing agreements with contract terms directly written into code. These contracts automatically enforce predefined rules once specific conditions are met, removing the need for intermediaries. In federated learning, smart contracts streamline governance and coordination by automating processes such as local model updates, managing communication rounds, and distributing rewards or incentives to participants. This decentralization ensure that the federated learning process follows agreed protocols without relying on a centralized authority. The ERC-20 standard [21] is employed in our system, offering a structured framework for token management, including functions for total supply tracking, balance management, and seamless token transfers. The approval mechanism within ERC-20 allows token holders to delegate permissions, while real-time event tracking ensures transparency in token transfers and balance updates. In this study, we integrate blockchain technology to enhance transparency, verifiability, and integrity within the federated learning framework. A decentralized ledger system, such as Vfchain, enables the verification and auditability of model updates without compromising data privacy. By storing cryptographic hashes of model updates and using a consensus mechanism enforced by smart contracts, blockchain ensures that tampering is prevented and only valid updates are recorded. This immutable ledger creates a comprehensive audit trail of all transactions, which is crucial for maintaining trust and accountability, particularly in sensitive fields like healthcare.  
However, integrating blockchain technology into our federated learning framework introduces some computational overhead. This is primarily due to the cryptographic operations required for transaction verification, smart contract execution, and ensuring the immutability of model updates. The overhead largely stems from the consensus mechanism, which involves validating and storing model updates on the blockchain. This can impact the time required for model aggregation in federated learning, particularly as the number of participants or the frequency of model updates increases. Although this computational burden may introduce delays, especially in real-time applications, it is a manageable trade-off in environments where security, transparency, and trustworthiness are the primary concerns.