Security-Oriented Architecture for Blockchain-Based Federated Learning in the Financial Industry

Financial institutions may be subject to financial fraud by malicious users because of the large amount of transaction data and sensitive user information involved. Therefore, it is crucial to design a machine learning model that can detect abnormal data in financial institutions. However, with the development of the economy and technology, the massive amount of user-generated data is distributed among various financial institutions, and how to enable multiple financial institutions to collaborate on anomalous data detection has become a new challenge. In this paper, we propose a blockchain-based federated learning architecture to assist multiple financial institutions to collaborate on anomaly detection. First, anomaly detection models are trained locally without sharing local data, which effectively protects data privacy. Second, the architecture introduces a differential privacy algorithm to protect data security in communication. Finally, to avoid communication bottlenecks that threaten data security, the architecture employs the aperiodic aggregation algorithm in which clients collaborate to reduce communication costs. Experimentally, a large number of experiments are conducted using three datasets to evaluate the proposed architecture. The experimental results show that the architecture is effective in detecting anomalous data and reducing communication costs.