University of Qom Journal of Engineering Management and Soft Computing 3116-0158 12 2 2026 04 01 Proposing a Data Governance Model for Fraud Detection in Executive Agencies Based on Federated Learning in a Cloud Computing Environment 109 140 4298 10.22091/jemsc.2026.14081.1309 EN Masoomeh Mojtabaee Ph.D. Student Department of Information Technology Management, Ki.C., Islamic Azad University , Kish, Iran, https://orcid.org/0009-0009-4675-360X 0009-0009-4675-360X Seyed Javad Iranbanfard Associate Prof., Department of Management, Shi.C., Islamic Azad University, Shiraz, Iran, https://orcid.org/0000-0003-2067-4371 0000-0003-2067-4371 Sara Najafzadeh Assistant Prof., Department of computer, YI.C., Islamic Azad University, Tehran, Iran, https://orcid.org/0000-0002-9415-6609 0000-0002-9415-6609 Mostafa Kolahdoozi Associate Prof., Department of Information Technology Management, SR.C. Islamic Azad University, Tehran, Iran, https://orcid.org/0009-0003-6839-4288 0009-0003-6839-4288 Journal Article 2025 10 04 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. https://jemsc.qom.ac.ir/article_4298_ff80c9606fb7fc3a3c056fea14883f50.pdf