Privacy-Preserving Cloud Financial Fraud Detection using Federated Learning and Distributed Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805039Keywords:
Federated learning, financial fraud detection, cloud computing, distributed intelligence, privacy preservation, machine learning, secure aggregation, differential privacy, cybersecurity, anomaly detectionAbstract
The rapid expansion of cloud-based financial services has increased the volume, velocity, and complexity of financial transactions, creating substantial opportunities for sophisticated fraud. Conventional fraud detection systems typically depend on centralized collection and processing of sensitive financial data, creating privacy risks, regulatory challenges, communication bottlenecks, and attractive targets for cyberattacks. This essay proposes a privacy-preserving cloud financial fraud detection framework that combines federated learning (FL) with distributed intelligence to detect fraudulent transactions without requiring participating financial institutions to share raw customer or transaction data. In the proposed approach, participating banks, payment providers, and financial organizations locally train machine-learning models using their own datasets. Only model parameters or privacy-protected updates are transmitted to a cloud-based coordination layer, where secure aggregation and distributed intelligence techniques support collaborative model improvement. Differential privacy, encryption, access control, and anomaly monitoring are incorporated to strengthen confidentiality and resistance against inference and poisoning attacks. The methodology evaluates the proposed framework using accuracy, precision, recall, F1-score, false-positive rate, communication cost, computational overhead, and privacy protection. The study anticipates that federated and distributed learning can provide competitive fraud-detection performance while substantially reducing exposure of sensitive financial information. The framework therefore offers a scalable and privacy-conscious foundation for collaborative financial fraud detection in cloud environments
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