A Blockchain-Integrated Federated Learning Approach for Secure Data Sharing and Privacy Protection in Multi-Device Communication

The secure transmission of communication data between different devices still faces numerous potential challenges, such as data tampering, data integrity, network attacks, and the risks of information leakage or forgery. This approach aims to handle the distributed trust issues of federated learning users and update data states rapidly. By modeling multi-source devices through federated learning, the model parameters and reputation values of participating devices are stored on the blockchain. This method incorporates factors such as experience, familiarity, and timeliness to more quickly gather reliable information about nodes to assess their behavior. Simulation results on the MNIST dataset show that when the proportion of selfish nodes is below 50%, the convergence time increases with the proportion of selfish nodes. Compared to advanced algorithms, the proposed model saves approximately 6% of interaction time. As the number of transactions significantly increases, the system's TPS (Transactions Per Second) decreases, with an average TPS of only 3079.35 when the maximum number of transactions is 4000. The proposed scheme can filter out high-quality data sources during real-time dynamic data exchange, enhancing the accuracy of federated learning training and ensuring privacy security.