A Novel Federated Learning System with Privacy Protection and Blockchain Consensus Incentive Mechanisms in Cloud-Edge Collaboration Scenarios

The data-driven transformation of industrial intelligence is becoming a key focus for the future of smart manufacturing. However, due to concerns about data privacy, the industry often faces a prevalent “data island” problem. Traditional federated learning ensures data privacy and usability to some extent, yet the C/S architecture's design model is susceptible to poisoning attacks, denial-of-service threats, and lacks consensus mechanisms and incentives among participants to ensure fairness in learning. This paper integrates federated learning into blockchain consensus protocols, proposing an FRConsensus algorithm based on model evaluation and stake election, which overcomes issues of passive participation and resource wastage during model training. Simultaneously, it introduces model watermarking and ECC public-key encryption mechanisms to secure parameter transmission. The experimental results demonstrate that the proposed federated learning system achieves superior model convergence. Furthermore, it is more effective in defending against poisoning attacks. In comparison to mainstream federated learning mechanisms based on consensus algorithms, it reduces the election latency by 20%, shortens the consensus latency by 8%, and increases the throughput by 12%.