Asynchronous Federated Learning for Personal Credit Assessment Based on Copula Reputation

Personal credit assessment, as an essential method to gauge an individual's credit status, exerts profound influences on personal economic life, risk control of financial institutions, establishment of social credit system, as well as the stableness of the overall economy. To address the challenges of delayed updates on bank information, malicious node attacks, issue of lacking user trust and data islands, this paper proposes a personal credit rating method based on asynchronous federated learning that is incentivized by copula reputation. This particular method creates a federated learning incentive mechanism based on copula variable dependence relationships that precisely assesses and measures the credibility of each distributed banking individual without acquiring original data, thereby preventing attacks from malicious nodes. Moreover, it puts forth a lightweight asynchronous federated learning mechanism according to each bank's local model's copula reputation, thereby optimizing the selection of participating nodes to reduce the system's overall cost, and settling the dilemma of bank information status update delays. Lastly, numerical results from real data sets indicate that the asynchronous federated learning personal credit assessment scheme based on copula reputation proposed has favorable accuracy, efficiency and enhanced security, efficiently safeguarding data privacy.