Incentivizing Robust Blockchain-FL FinTech Services for Heterogeneous Financial Institutions
PI: Dr Edith Cheuk Han NGAI
Abstract:
In this project, we will design and develop an incentivized blockchain and federated learning (FL) framework that allows financial institutions with heterogeneous resources to co-train financial machine learning models in a robust and privacy-preserving manner. This blockchain-FL framework considers financial institutions with heterogeneous resources, including computation resources, amount and quality of data, and reliability. Our incentivized blockchain-FL framework will encourage financial institutions to participate in distributed machine learning training and blockchain validation processes. Contribution evaluation and incentive allocation will be conducted to give reliable participating financial institutions with higher rewards. The project will lead to a powerful and robust global model for financial institutions to perform their financial services, such as credit risk assessment and customer portfolio analysis.