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Title: Data security collaboration mechanism of college student innovation and entrepreneurship education platform combining federated learning and differential privacy

Authors: Jin Jiang; Hao Wang; Chengqi Ye; Weining Yang

Addresses: School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China ' School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China ' School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China ' School of Economics and Management, Hefei Normal University, Hefei, 230601, China

Abstract: To address the challenges of data silos and privacy in cross-institutional collaboration, this study introduces a secure data collaboration framework combining federated learning (FL) and differential privacy (DP). The framework enables collaborative model training by keeping data local while using client-side DP to counter privacy threats like membership inference attacks. An adaptive privacy budget allocation (APBA) strategy further optimises the utility-privacy balance. Evaluations on real educational datasets show the framework maintains strong privacy (attack success <5%), achieves a 95% F1 score - comparable to centralised training - and improves communication efficiency by ~40%. This work provides a technical foundation for building secure and efficient cross-institutional platforms in innovation and entrepreneurship education.

Keywords: federated learning; FL; differential privacy; DP; data security; innovation and entrepreneurship education; collaboration mechanism; privacy protection.

DOI: 10.1504/IJICT.2026.153526

International Journal of Information and Communication Technology, 2026 Vol.27 No.47, pp.45 - 60

Received: 05 Nov 2025
Accepted: 16 Dec 2025
Published online: 12 May 2026 *