Privacy-Preserving Federated Graph Learning for Lecturer Digital Competence Enhancement
DOI:
https://doi.org/10.59653/ijmars.v4i02.2332Keywords:
federated learning, graph neural networks, digital competence, privacy preservation, higher education, professional developmentAbstract
This study proposes a federated heterogeneous graph neural network framework for enhancing lecturer digital competence through privacy-preserving, cross-institutional collaboration. Traditional recommender systems frequently encounter challenges associated with data silos and privacy constraints, thereby limiting their capacity to deliver personalized professional development recommendations. The proposed framework addresses these challenges by modeling lecturer–institution–resource interactions as a heterogeneous graph, wherein nodes represent lecturers, institutions, courses, and resources, while edges capture their complex relational structures. A relation-aware graph attention network is employed to learn node embeddings locally, thereby enabling institutions to train models without sharing raw data. Furthermore, the framework integrates federated split learning with differential privacy, ensuring that intermediate outputs are perturbed with Gaussian noise prior to secure aggregation. The global model generates personalized recommendations by computing compatibility scores between lecturer and resource embeddings, subsequently ranking these to suggest relevant micro-courses or workshops. Experimental results demonstrate that the framework achieves 94.3% of centralized model performance while maintaining provable (1.0, 10⁻⁵)-differential privacy guarantees, significantly outperforming existing federated baselines in both recommendation accuracy and system efficiency. These findings contribute to the growing body of knowledge on data-driven human resource development and institutional data governance in higher education.
Downloads
References
Alhindi, A., Al-Ahmadi, S., & Ben Ismail, M. M. (2025). Advancements and challenges in privacy-preserving split learning: Experimental findings and future directions. International Journal of Information Security, 24, 125. https://doi.org/10.1007/s10207-025-01045-9
Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2019). Local differential privacy for deep learning. IEEE Internet of Things Journal, 7(7), 5827–5842. https://doi.org/10.1109/JIOT.2019.2952146
Bahari, A., & Liu, Y. (2025). AI integration in EFL teacher development: A mixed-methods evaluation of digital competency, professional trajectories, and pedagogical innovation within adaptive learning ecosystems. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2025.2591251
Chen, H., Zhu, T., Zhang, T., Zhou, W., & Yu, P. S. (2023). Privacy and fairness in federated learning: On the perspective of tradeoff. ACM Computing Surveys, 56(2), 1–37. https://doi.org/10.1145/3606017
Dang, T. D., Phan, T. T., Vu, T. N. Q., La, T. D., & Pham, V. K. (2024). Digital competence of lecturers and its impact on student learning value in higher education. Heliyon, 10(17), e37318. https://doi.org/10.1016/j.heliyon.2024.e37318
Dankar, F., El Emam, K., Neisa, A., & Roffey, T. (2012). Estimating the re-identification risk of clinical data sets. BMC Medical Informatics and Decision Making, 12, 66. https://doi.org/10.1186/1472-6947-12-66
Dwork, C., Naor, M., Pitassi, T., & Rothblum, G. N. (2010). Differential privacy under continual observation. In Proceedings of the 42nd ACM Symposium on Theory of Computing (pp. 715–724). https://doi.org/10.1145/1806689.1806787
Feng, Y., & Qian, Q. (2025). PPFedGNN: An efficient privacy-preserving federated graph neural network method for social network analysis. IEEE Transactions on Computational Social Systems, 12(1), 310–323. https://doi.org/10.1109/TCSS.2024.3398399
Gursoy, M. E., Inan, A., Nergiz, M. E., & Saygin, Y. (2016). Privacy-preserving learning analytics: Challenges and techniques. IEEE Transactions on Learning Technologies, 10(1), 68–81. https://doi.org/10.1109/TLT.2016.2607747
Huang, Q., & Chen, J. (2024). Enhancing academic performance prediction with temporal graph networks for massive open online courses. Journal of Big Data, 11, 71. https://doi.org/10.1186/s40537-024-00934-5
Ishiwatari, T., Yasuda, Y., Miyazaki, T., & Goto, J. (2020). Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 7360–7370). https://doi.org/10.18653/v1/2020.emnlp-main.597
Li, N., Lyu, M., Su, D., & Yang, W. (2017). Differential privacy: From theory to practice. Morgan & Claypool Publishers. https://doi.org/10.1007/978-3-031-02350-7
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Liesa-Orus, M., Latorre-Cosculluela, C., & Arce-Romeral, L. (2023). Digital competence in university lecturers: A meta-analysis of teaching challenges. Education Sciences, 13(2), 125. https://doi.org/10.3390/educsci13020125
Liu, R., Xing, P., Deng, Z., Li, A., Guan, C., & Yu, H. (2024). Federated graph neural networks: Overview, techniques, and challenges. IEEE Transactions on Neural Networks and Learning Systems, 35(7), 8906–8920. https://doi.org/10.1109/TNNLS.2022.3229876
Liu, Y., Li, H., & Hao, M. (2024). SVFGNN: A privacy-preserving vertical federated graph neural network model training framework based on split learning. Peer-to-Peer Networking and Applications, 17, 2513–2527. https://doi.org/10.1007/s12083-024-01710-5
Liu, Y., Yang, C., Ma, J., Xu, W., & Hua, Z. (2019). A social recommendation system for academic collaboration in undergraduate research. Expert Systems, 36(6), e12462. https://doi.org/10.1111/exsy.12462
Ma, C., Molnár, B., Tarcsi, Á., & Benczur, A. (2022). Knowledge enriched schema matching framework for heterogeneous data integration. In 2022 IEEE 2nd International Conference on Data Intelligence and Security (pp. 172–179). https://doi.org/10.1109/ICDIS55630.2022.00034
Messinis, S., Protonotarios, N., & Doulamis, N. (2024). Differentially private client selection and resource allocation in federated learning for medical applications using graph neural networks. Sensors, 24(14), 4537. https://doi.org/10.3390/s24144537
Near, J., Darais, D., Buckley, D., & Durkee, M. (2024). Privacy attacks in federated learning (NIST Technical Report). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8523
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71, 137–161. https://doi.org/10.1007/s11423-023-10203-6
Renda, A., Ducange, P., Marcelloni, F., Sabella, D., Filippou, M. C., Nardini, G., Stea, G., Virdis, A., Micheli, D., Rapuano, E., & Detti, A. (2022). Federated learning of explainable AI models in 6G systems: Towards secure and automated vehicle networking. Information, 13(8), 395. https://doi.org/10.3390/info13080395
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2018). Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357–370. https://doi.org/10.1109/TKDE.2018.2833443
Sun, Z., Zhao, Z., Shao, R., Zou, Y., Li, C., & Xin, Y. (2024). Survey on privacy-preserving techniques for graph neural networks in federated learning paradigm. In 2024 IEEE International Conference on Big Data (pp. 5802–5811). https://doi.org/10.1109/BigData62323.2024.10825058
Thapa, C., Arachchige, P. C. M., Camtepe, S., & Sun, L. (2022). SplitFed: When federated learning meets split learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8485–8493. https://doi.org/10.1609/aaai.v36i8.20825
Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018). Split learning for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564. https://doi.org/10.48550/arXiv.1812.00564
Wang, J., Li, Y., & Qian, Y. (2025). AI-assisted teaching and learning: A real application of course design, assessment and evaluation in higher education. In Proceedings of the International Conference on Education and Artificial Intelligence. https://doi.org/10.1007/978-981-96-4476-3_6
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. In The World Wide Web Conference (pp. 2022–2032). https://doi.org/10.1145/3308558.3313562
Wu, C., Wu, F., Cao, Y., Huang, Y., & Xie, X. (2021). FedGNN: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925. https://doi.org/10.48550/arXiv.2102.04925
Wu, X., Zhang, Y., Shi, M., Li, P., Li, R., & Xiong, N. N. (2022). An adaptive federated learning scheme with differential privacy preserving. Future Generation Computer Systems, 127, 362–372. https://doi.org/10.1016/j.future.2021.09.015
Xiong, X., Liu, S., Li, D., Cai, Z., & Niu, X. (2020). A comprehensive survey on local differential privacy. Security and Communication Networks, 2020, 8829523. https://doi.org/10.1155/2020/8829523
Yan, B., Cao, Y., Wang, H., Yang, W., Du, J., & Yu, P. S. (2024). Federated heterogeneous graph neural network for privacy-preserving recommendation. In Proceedings of the ACM Web Conference 2024 (pp. 562–573). https://doi.org/10.1145/3589334.3645560
Yao, Y., Jin, W., Ravi, S., & Joe-Wong, C. (2023). FedGCN: Convergence-communication tradeoffs in federated training of graph convolutional networks. In Advances in Neural Information Processing Systems, 36, 25567–25581. https://doi.org/10.48550/arXiv.2201.12433
Yurdem, B., Kuzlu, M., Gullu, M. K., Catak, F. O., & Tabassum, M. (2024). Federated learning: Overview, strategies, applications, tools and future directions. Heliyon, 10(21), e39689. https://doi.org/10.1016/j.heliyon.2024.e39689
Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019). Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 793–803). https://doi.org/10.1145/3292500.3330961
Zhang, S. (2025). Exploring challenges and opportunities in the AI-driven professional development of higher education faculty. International Journal of Learning and Teaching, 11(2), 78–85. https://doi.org/10.18178/ijlt.11.2.78-85
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Truong Xuan Nam
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
