Abstract:

In order to address the potential issues of privacy leakage,a single point of failure,and poisoning attacks in traditional federated learning application center servers,a fair federated learning framework based on the alliance chain is proposed.Through the mutual selection of leader nodes and consensus committee nodes in each round,secure aggregation and updating of data are achieved,ensuring the decentralized and distributed characteristics of the system.Meanwhile,by leveraging the immutability of the blockchain and its resilience to single-point attacks,a client-level data quality assessment method is designed within the consensus mechanism to provide necessary quantitative metrics for multi-party training,ensure the transparency and traceability of evaluation results,and optimize the node selection process,thereby ensuring the prioritization of high-quality clients.To improve the fairness of node selection,an improved algorithm based on the Shapley value is proposed that incorporates the historical behavioral performance of clients to make contribution evaluation more flexible and accurate,thus reducing the proportion of low-quality nodes in contribution evaluation and mitigating the negative impact of low-quality data on model training.Experimental results show that the scheme significantly enhances the fairness of leader node elections and the accuracy of client marginal contribution assessments,while maintaining the model prediction accuracy.Through a dynamic node reward mechanism,the long-term fairness of the system is ensured,effectively addressing fairness issues in the alliance chain.

Key words: federated learning, Shapley value, fairness, consensus mechanism