Secure Blockchain-Empowered Federated Learning for FinTech
PI: Dr. Edith C.H. Ngai, HKU
The project aims to design and develop a secure and privacy-preserving blockchain-empowered federated learning framework for FinTech applications. The proposed Federated Learning (FL) framework preserves data privacy by enabling collaborative machine learning for distributed financial organizations without disclosing their private datasets. The FL framework will be fully integrated with blockchain technology to ensure data integrity and secure data aggregation in model training. The local training updates from different organizations will be aggregated to build global models supporting Fintech applications, such as credit evaluation and fraud prediction. We will implement and evaluate the performance of our blockchain-empowered federated learning framework and demonstrate a Fintech application for credit approval.