Research on the Application of Blockchain Consensus Mechanisms in Federated Learning for Privacy Protection

Abstract

Federated learning, as a distributed machine learning method is facing significant challenges in data privacy protection at present. This study aims to enhance the privacy protection capability of federated learning by integrating blockchain consensus mechanisms, as well as proposing a novel solution by designing a decentralized federated learning framework using consensus mechanisms such as Proof of Stake (PoS), and implementing smart contracts to automate key processes. The research results demonstrate that this approach effectively enhances the security and transparency of data processing while ensuring the efficiency and scalability of the system. Furthermore, the adoption of homomorphic encryption technology further ensures the security and integrity of data during transmission.