Journal of Engineering, Project, and Production Management, 2027, 17(2), 2026-0078
Enterprise Data Governance Combining Federated Transfer Learning and Security Audit
1 Instructor, Guangzhou College
of Technology and Business, Guangzhou 510800, China, E-mail: wangjinhui@gzgs.edu.cn
(corresponding author).
2 Instructor, Digital Urban Operation and Governance Centre
of Chengkou County, Chongqing 405900, China
3 Vice President, Guangzhou Shiyuan Electronic Technology
Co., Ltd., Guangzhou, China
Project Management
Received June 1, 2026; Revised July 27, 2026, August 4, 2026, September 29, 2026; Accepted October 16, 2026
Available online October 26, 2026
Abstract: Due to the problems of dispersed business data, inconsistent regulatory interpretations and limited cross-domain modeling at the enterprise level, this paper puts forward a data governance model based on federated transfer learning and security auditing. The preprocessing stage adds salted pseudonymization, entity masking, rare-category pooling, 30-minute window aggregation, and lineage/access-log feature construction to ensure reproducibility without revealing raw records. The method builds metadata, lineage, access behavior and rule-hitting features locally in each business domain and achieves cross-domain knowledge transfer via transfer gating and alignment loss. At the same time, it records summary hashing, responsible parties, policy consistency and lineage continuity in audit quality scores, adds a risk-suppression aggregation mechanism, and synchronously links the model update process to the governance traceability process. Experiments are based on financial, procurement, logistics, sales and compliance data from a large local group from January 2024 to December 2025. According to the results above, the proposed method achieves an AUC of 0.957, a macro-F1 score of 0.912, a recall of 0.926, and an audit trace completeness of 0.987. It is 2.1 and 2.3 percentage points higher than the best-performing comparative method. Extra imbalance, threshold-sensitivity and edge deployment tests show that the transfer gate reduces negative transfer in a 1:50 class imbalance, and audit-suppressed aggregation maintains real-time latency below 160ms under the tested edge configuration. Thus, cross-domain risk identification and strategy implementation can be achieved in a closed-loop manner without aggregating raw data, and a technical path for enterprise data governance that balances accuracy, compliance, and feasibility has been provided. A separate public HDFS log benchmark supplies a limited proxy stress test; it does not validate enterprise audit completeness or reproduce an accuracy advantage over FedAvg.
Keywords: Enterprise data governance; federated transfer learning; security audit; cross-domain risk identification; traceable governance.
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Citation: Wang, J., Lei, Y., and Wang, R. (2027). Enterprise Data Governance Combining Federated Transfer Learning and Security Audit. Journal of Engineering, Project, and Production Management, 17(2), 2026-0078.
