Federated Learning in Banking: Preserving Data Privacy While Improving AI Model Performance

Abstract

Federated learning (FL) helps banks meet data-driven AI application demand and privacy and security standards. Using federated learning, banks may train AI models without central servers. GDPR and CCPA-compliant solution safeguards consumer data. FL promotes collaborative learning without bank data loss. Complex AI systems evaluate many institution datasets. 

Federated learning architecture, methods, and algorithms for distributed training are described. FedAvg and FL multi-party computation provide privacy. The study studies communication protocols and optimisation methods to sync model updates across several nodes, which may be difficult owing to bandwidth, latency, and model convergence issues. Local model training, aggregation, validation, and federated learning deployment data preparation are handled. The study revealed that federated learning scales banking machine learning systems for real-time fraud detection, credit scoring, risk management, and personalised financial services. 

Use real-world examples to demonstrate how federated learning may improve AI model performance and data security. Fl lets banks and other financial organisations construct predictive models to identify fraud and unusual activity without retaining transaction data. This lowers data breaches and rule-breaking. Examples include AI, data security, and auditability. This work critically examines federated learning difficulties such data distributions, synchronisation, and model fairness across data sources. Adjustable learning rates and federated data augmentation are advised. 

Bank federated learning demands plenty of processing power for local model training, aggregation, and distant node communication. Comparing data privacy versus computational efficiency. DLT and FL simplify and secure collaborative training. These sessions illustrate homomorphic encryption's model training update protection. Collaboration partners assess data security and trust. 

Federated banking learning may be operational and technological. Concerningly, non-IID (independent and identically distributed) data may degrade models. Customised federated learning and transfer may assist. The research also analyses how federated learning algorithms may scale up and work with different financial institutions' infrastructure, including smaller banks with less computing capability. 

Federated learning may impact model performance and financial data privacy. Without centralised data, banks may cooperate on analytics. Federation learning and blockchain may improve regulatory and consumer data transparency, traceability, and integrity. This convergence may improve client trust by providing safe, privacy-protecting banking solutions.