With the rapid development of the digital economy, the demand for data collaboration among enterprises is constantly increasing. Data security and privacy protection have become obstacles to the in-depth development of cross-enterprise business analysis. To address the problems of privacy leakage, communication overhead, and heterogeneous data processing in traditional centralized modeling, this paper proposes an efficient Federated Graph Neural Network (FGNN) for cross-enterprise business analysis. In order to protect data privacy and realize cross-domain feature learning and efficient aggregation of heterogeneous enterprise graph data, this study first analyzes the heterogeneity and non-independent and identically distributed characteristics of cross-enterprise data, as well as the communication and security bottlenecks of federated learning. Secondly, an efficient communication mechanism based on sparse gradient compression and weighted aggregation is designed, and then homomorphic encryption and federated differential privacy technologies are introduced to enhance security protection.

Yiting Hong. An Efficient Federated Graph Neural Network Framework for Cross-Enterprise Business Analysis. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 170-176. https://doi.org/10.38007/SESR.2025.060218.

[1] Tan, Z., Li, C., Chen, H., & Du, B. (2024). FedSSP: Federated graph learning with spectral bias personalization. NeurIPS 2024 Proceedings, 1–21.

[2] Huang, W., Wan, G., Ye, M., & Du, B. (2023). Federated graph semantic and structural learning. IJCAI 2023 Proceedings, 3829–3836.

[3] Li, Z., Wang, X., Li, Q., & Chen, B. (2024). Privacy-preserving graph embedding based on local differential privacy. Proceedings of ACM CIKM 2024.

[4] Li, Y., Li, J., & Guo, S. (2024). Secure and efficient multi-key aggregation for federated learning via homomorphic encryption. Information Sciences, 660, 119838.

[5] Wu, H. (2025). The Commercialization Path of Large Language Models in Start-Ups. European Journal of Business, Economics & Management, 1(3), 38-44.

[6] Ye, J. (2025). Optimization and Application of Gesture Classification Algorithm Based on EMG. Journal of Computer, Signal, and System Research, 2(5), 41-47.

[7] Xu Q. AI-Based Enterprise Notification Systems and Optimization Strategies for User Interaction. European Journal of AI, Computing & Informatics, 2025, 1(2): 97-102.

[8] Huang, J. (2025). Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning. European Journal of Business, Economics & Management, 1(3), 104-110.

[9] Chen M. Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems. Advances in Computer and Communication, 2025, 6(4).

[10] Qi, Y. (2025). Data Consistency and Performance Scalability Design in High-Concurrency Payment Systems. European Journal of AI, Computing & Informatics, 1(3), 39-46.

[11] Jiang, Y. (2025). Application and Practice of Machine Learning Infrastructure Optimization in Advertising Systems. Journal of Computer, Signal, and System Research, 2(6), 74-81.

[12] Zou, Y. (2025). Automated Reasoning and Technological Innovation in Cloud Computing Security. Economics and Management Innovation, 2(6), 25-32.

[13] An, C. (2025). Study on Efficiency Improvement of Data Analysis in Customer Asset Allocatior. Journal of Computer, Signal, and System Research, 2(6), 57-65.

[14] Huang, J. (2025). Optimization and Innovation of AI-Based E-Commerce Platform Recommendation System. Journal of Computer, Signal, and System Research, 2(6), 66-73.

[15] Zhang, X. (2025). Optimization of Financial Fraud Risk Identification System Based on Machine Learning. Journal of Computer, Signal, and System Research, 2(6), 82-89.

[16] Wang, Y. (2025). Exploration and Clinical Practice of the Optimization Path of Sports Rehabilitation Technology. Journal of Medicine and Life Sciences, 1(3), 88-94.

[17] Li W. The Influence of Financial Due Diligence in M&A on Investment Decision Based on Financial Data Analysis. European Journal of AI, Computing & Informatics, 2025, 1(3): 32-38.

[18] Sheng, C. (2025). Innovative Application and Effect Evaluation of Big Data in Cross-Border Tax Compliance Management. Journal of Computer, Signal, and System Research, 2(6), 40-48.

[19] Sheng, C. (2025). Research on the Application of AI in Enterprise Financial Risk Management and Its Optimization Strategy. Economics and Management Innovation, 2(6), 18-24.

[20] Tu, X. (2025). Optimization Strategy for Personalized Recommendation System Based on Data Analysis. Journal of Computer, Signal, and System Research, 2(6), 32-39.