Federated Learning Empowered Recommendation Model for Financial Consumer Services

In recent years, recommendation systems have gained popularity in the financial services industry. These systems offer personalized recommendations to consumers based on their distinct preferences, behaviors, and historical data. Centralized data storage and processing used in traditional recommendation systems can raise privacy and security concerns. In light of these challenges, in this paper, a federated learning-empowered recommendation model (FLRM) is proposed that utilizes federated learning and blockchain technology. In our proposed model, the central server coordinates model aggregation and communicates with the blockchain network. In FLRM, financial institutions hold their data in private blockchains while participating in the federated learning process. Federated learning provides a solution to these challenges by enabling privacy-preserving collaborative model training across multiple distributed data sources. Blockchain technology enhances the security and transparency of the federated learning model by providing a decentralized and tamper-proof mechanism for data storage and management. By decentralizing the recommendation system, FLRM enhances user privacy, reduces data transfer overhead, and builds trust through transparency. The integration of smart contracts in this proposed model facilitates secure and automated transactions. The proposed approach represents a significant step forward in creating a more secure, privacy-preserving, and effective recommendation system model.