Proposing a Data Governance Model for Fraud Detection in Executive Agencies Based on Federated Learning in a Cloud Computing Environment
نویسندگان
1 Associate Prof., Department of Management, Shi.C., Islamic Azad University, Shiraz, Iran, https://orcid.org/0000-0003-2067-4371
2 Assistant Prof., Department of computer, YI.C., Islamic Azad University, Tehran, Iran, https://orcid.org/0000-0002-9415-6609
3 Ph.D. Student Department of Information Technology Management, Ki.C., Islamic Azad University , Kish, Iran, https://orcid.org/0009-0009-4675-360X
4 Associate Prof., Department of Information Technology Management, SR.C. Islamic Azad University, Tehran, Iran, https://orcid.org/0009-0003-6839-4288
doi
10.22091/jemsc.2026.14081.1309چکیده
With the growing volume of data interactions and increasing complexity of oversight in executive agencies, the need for innovative approaches to fraud detection and data governance has become more pressing than ever. Given that data is stored in a distributed manner, cloud computing emerges as an effective solution. However, security concerns hinder direct data exchange between organizations. To address this challenge, the present study proposes a decentralized, data-driven governance model based on federated learning and cloud infrastructure. In this model, each organization preprocesses its data at the edge and extracts fraud-related features. These results are then transmitted to a central server, where deep learning techniques are used to predict new inter-organizational fraud patterns. This approach preserves confidentiality and enables collaborative analysis without requiring data aggregation. Experimental results show that the proposed method reduces computational complexity by 60% and achieves a fraud detection accuracy of 97.6%, demonstrating its high effectiveness in multi-organizational and distributed environments.
