A Scalable Federated Learning Architecture for Privacy-Preserving Financial Data Processing

Due to the quick digital transformation of the banking and financial services sector, financial data is now larger, more sensitive, and easier to find.Conventional centralized machine learning solutions pose serious privacy, security, and regulatory issues to businesses since they need gathering user data in one place.Federated Learning (FL) is a novel method that enables users to train models collectively without exchanging raw data.The PCI-DSS, GDPR, and RBI privacy laws are all met by this.In addition to incorporating features like safe aggregation, homomorphic encryption, differential privacy, and blockchain-based auditability, this paper provides a full federated learning system for financial analytics that protects privacy.The study illustrates how FL might reduce the risks associated with central data storage, hence enhancing financial forecasts, risk modelling, fraud detection, and credit rating.A thorough experimental analysis is presented to compare FL to conventional centralized approaches on important performance metrics as computation load, accuracy, privacy protection, and communication efficiency.The results demonstrate that FL maintains model performance competitiveness while significantly improving privacy and regulatory compliance.Additionally, in distributed financial contexts, the suggested blockchain-based auditability layer guarantees the permanence of transparency, verifiability, and recording.The essay also covers potential difficulties, how to apply the concepts, and the most effective methods for handling actual financial systems.Our study concludes by demonstrating the great potential of Federated Learning as a safe, scalable, and legally permissible alternative for next-generation financial analytics.