Research on Validation and Incentive Mechanism of Federated Learning Model Based on Blockchain

This paper presents a new federated learning framework leveraging blockchain technology to enhance the security and availability of privacy-preserving computations under semi-honest and malicious models. Federated learning, a novel approach to machine learning, trains models collaboratively without sharing local data, thereby safeguarding privacy. Despite its advantages, federated learning faces significant challenges, including the potential disclosure of private information, the unreliability of model parameter uploads, and a lack of effective incentive mechanisms. This study addresses these issues by proposing a blockchain-based validation and scoring mechanism that ensures the integrity of model validations and participant contributions. Moreover, we introduce a reputation-based incentive mechanism that encourages data owner participation by distributing rewards according to their contributions. Empirical results from simulations on the MNIST dataset demonstrate that our framework effectively promotes high-quality model aggregation and deters malicious activities in training processes.