ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES
DOI:
https://doi.org/10.33003/fjs-2025-0905-3207Keywords:
Anti-Money Laundering (AML), Data Privacy, Differential Privacy (DP), Federated Learning (FL), Homomorphic Encryption (HE), Loan Default Prediction, Secure Multi-Party Computation (SMPC)Abstract
This paper explores the application of Federated Learning (FL) in the financial sector, focusing on enhancing security and privacy in key areas such as fraud detection, Anti-Money Laundering (AML) compliance, and biometric authentication systems. FL enables collaborative model training across multiple financial institutions without sharing sensitive transaction data, thereby preserving privacy while improving the accuracy of fraud detection models. In AML compliance, FL facilitates the development of robust models by leveraging diverse datasets, enhancing the ability to detect suspicious activities. Moreover, FL strengthens biometric authentication systems by decentralizing model training, reducing the risks of data breaches, and ensuring compliance with privacy regulations. The paper also evaluates the performance of a loan default prediction model trained using FL, highlighting challenges with class imbalance and model bias toward the majority class. The classification report indicates high recall (98%) but also shows a potential for misclassifying non-default cases, leading to a moderate precision (81%) and an F1-score of 89%. The model's AUC of 0.69 suggests moderate discriminatory power, with room for improvement in its ability to differentiate between default and non-default cases. The model achieves an overall accuracy of 80%. Despite these challenges, it demonstrates good generalization capabilities while maintaining the privacy of client data, presenting a promising approach to secure financial transaction analysis.
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Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Access, 8, 140699140725. https://doi.org/10.1109/ACCESS.2020.3013541
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Fragulis, G. F., Papatsimouli, M., Lazaridis, L., & Skordas, I. A. (2021). An Online Dynamic Examination System (ODES) based on open source software tools. Software Impacts, 7, 100046. https://doi.org/10.1016/j.simpa.2020.100046
Godfrey Perfectson Oise. (2023). A Framework on E-Waste Management and Data Security System. International Journal on Transdisciplinary Research and Emerging Technologies, 1(1).
Gray, M., Fox, N., Gordon, J. E., Brilha, J., Charkraborty, A., Garcia, M. D. G., Hjort, J., Kubalkov, L., Seijmonsbergen, A. C., & Urban, J. (2024). Boundary of ecosystem services: A response to. Journal of Environmental Management, 351, 119666. https://doi.org/10.1016/j.jenvman.2023.119666
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Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., DOliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascn, A., Ghazi, B., Gibbons, P. B., Zhao, S. (2019). Advances and Open Problems in Federated Learning (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1912.04977
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021). Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges. IEEE Communications Surveys & Tutorials, 23(3), 17591799. https://doi.org/10.1109/COMST.2021.3090430
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Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020). A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854. https://doi.org/10.1016/j.cie.2020.106854
Li, Z., He, S., Chaturvedi, P., Kindratenko, V., Huerta, E. A., Kim, K., & Madduri, R. (2024). Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing ResourcesA Case Study on Federated Fine-tuning of LLaMA 2 (arXiv:2402.12271). arXiv. https://doi.org/10.48550/arXiv.2402.12271
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021). A survey on security and privacy of federated learning. Future Generation Computer Systems, 115, 619640. https://doi.org/10.1016/j.future.2020.10.007
Nathan George. (2020). All Lending Club loan data [Dataset]. Kaggle online data repository. https://www.kaggle.com/datasets/wordsforthewise/lending-club
Nevrataki, T., Iliadou, A., Ntolkeras, G., Sfakianakis, I., Lazaridis, L., Maraslidis, G., Asimopoulos, N., & Fragulis, G. F. (2023). A survey on federated learning applications in healthcare, finance, and data privacy/data security. 120015. https://doi.org/10.1063/5.0182160
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Oise, G., & Konyeha, S. (2024). E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH. FUDMA JOURNAL OF SCIENCES, 8(3), 1724. https://doi.org/10.33003/fjs-2024-0804-2579
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Oise, G. P., Nwabuokei, O. C., Akpowehbve, O. J., Eyitemi, B. A., & Unuigbokhai, N. B. (2025). TOWARDS SMARTER CYBER DEFENSE: LEVERAGING DEEP LEARNING FOR THREAT IDENTIFICATION AND PREVENTION. FUDMA JOURNAL OF SCIENCES, 9(3), 122128. https://doi.org/10.33003/fjs-2025-0903-3264
Papatsimouli, M., Lazaridis, L., Ziouzios, D., Dasygenis, M., & Fragulis, G. (2022). Internet Of Things (IoT) awareness in Greece. SHS Web of Conferences, 139, 03013. https://doi.org/10.1051/shsconf/202213903013
Rells, J., & Joseph, W. (2025). Federated Learning for Secure Financial Transactions. https://www.researchgate.net/publication/389389123
Tsakiris, G., Papadopoulos, C., Patrikalos, G., Kollias, K.-F., Asimopoulos, N., & Fragulis, G. F. (2022). The development of a chatbot using Convolutional Neural Networks. SHS Web of Conferences, 139, 03009. https://doi.org/10.1051/shsconf/202213903009
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Zacharis, G., Gadounas, G., Tsirtsakis, P., Maraslidis, G., Assimopoulos, N., & Fragulis, G. (2022). Implementation and Optimization of Image Processing Algorithm using Machine Learning and Image Compression. SHS Web of Conferences, 139, 03014. https://doi.org/10.1051/shsconf/202213903014
Zelios, A., Grammenos, A., Papatsimouli, M., Asimopoulos, N., & Fragulis, G. (2022). Recursive neural networks: Recent results and applications. SHS Web of Conferences, 139, 03007. https://doi.org/10.1051/shsconf/202213903007
Zhang, Y., Bai, G., Li, X., Nepal, S., & Ko, R. K. L. (2021). Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2104.13050
Published
2025-05-31
How to Cite
Oise, G. P., Unuigbokhai, N. B., Akilo, B. E., Nwabuokei, O. C., Odimayomi, J. A., Bakare, S. K., & Atake, O. M. (2025). ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES. FUDMA JOURNAL OF SCIENCES, 9(5), 80 - 86. https://doi.org/10.33003/fjs-2025-0905-3207
Issue
Section
Research Articles
FUDMA Journal of Sciences
