A Cloud-Centric Data Governance Model Based on Deep Interactive Hybrid Federated Learning for Anomaly Detection in the Financial Sector
Keywords:
Data Governance, Cloud Computing, Federated Learning, Feature Selection, Chaotic Harmony Search Algorithm, Deep Learning, Financial Anomaly DetectionAbstract
The purpose of this study was to develop a cloud-centric data governance model based on deep interactive hybrid federated learning to improve the accuracy of financial anomaly detection while preserving data privacy and enhancing scalability in distributed financial environments. This study proposed an intelligent framework integrating cloud computing, federated learning, feature selection, and deep learning. Financial features were distributed between two federated nodes, where the first node employed a filter-based feature selection strategy and the second utilized a wrapper-based approach. In both federated environments, a Chaotic Harmony Search Algorithm (CHSA) was used to optimize feature selection. The selected features were subsequently aggregated on a central server and evaluated through a deep learning core based on a Convolutional Neural Network (CNN). The proposed framework was implemented and tested using the PaySim financial transaction dataset, and its performance was compared with several conventional, ensemble, and state-of-the-art classification methods. The results demonstrated the consistent superiority of the proposed framework across all experimental scenarios. The CNN classifier achieved the highest accuracy of 98.49%, outperforming KNN (92.69%), DT (95.23%), RF (95.28%), Bagging (95.28%), and XGBoost (94.93%). Furthermore, the proposed approach surpassed previously reported methods, including ENN (97%), Stacked-RF (96%), 1D-CNN (90%), ResNet-GRU (89.6%), Stacked Autoencoder (85.2%), ADASYNN (79%), and SMOTEENN (73%). These findings confirm the effectiveness of combining interactive federated learning, multi-stage feature selection, chaotic optimization, and deep learning for financial anomaly detection. The findings indicate that integrating cloud-centric data governance, federated learning, chaotic harmony search optimization, and deep learning significantly enhances financial anomaly detection performance while maintaining privacy and security requirements. The proposed framework offers a scalable, privacy-preserving, and highly accurate solution for modern financial systems and fraud detection applications.
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Copyright (c) 2025 Masoomeh Mojtabaee (Author); Seyed Javad Iranbanfard; Sara Najafzadeh, Mostafa Kolahdoozi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
