Blockchain-Based Personalized Federated Learning Leveraging the SMPC protocol

Federated Learning (FL) has emerged as a ground-breaking strategy to address data privacy in various domains. This paper investigates the potential of integrating Blockchain with FL. Federated Learning, known for its data privacy benefits, faces hurdles such as communication overhead and data security. Blockchain, leveraging decentralization, immutability, and consensus mechanisms, fortifies Federated Learning. To address the need for personalized insights while upholding data privacy, we introduce a novel approach-Personalized Federated Learning leveraging Secure Multi-Party Computation (SMPC) Encryption. This approach enables the development of individu-alized healthcare models while preserving the confidentiality of sensitive data. To bolster trust, transparency, and security, we propose a Committee Consensus Mechanism. This mechanism enhances consensus decisions by incorporating a committee of trusted participants, and public validation ensures transparency and external verification. To further enhance security and prevent model poisoning, we employ smart contract to verify the integrity of local models, thus ensuring the overall integrity and reliability of the FL process. In the context of data storage, we advocate for the use of the InterPlanetary File System (IPFS) for tamper-proof storage, ensuring data integrity and protection against unauthorized access. These contributions collectively establish a robust foundation for secure, transparent, and personalized systems.