Constructing a Personalized Learning Path Recommendation Model Based on Federated Learning
Main Article Content
Abstract
To address the conflict between model generalization and personalized recommendation in distributed educational environments, this paper constructs a personalized learning path recommendation model based on federated learning. Such privacy-preserving collaborative learning frameworks are also valuable for intelligent information processing and distributed decision-making in modern electromagnetic communication and edge computing systems, where data sharing is often restricted. The proposed model employs a Graph Neural Network (GNN)-Transformer hybrid encoder that combines knowledge graphs with learning behavior sequences to accurately capture knowledge transfer relationships. A dynamic knowledge distillation aggregation strategy is introduced to generate soft labels from the global model for guiding local optimization, thereby preserving personalized characteristics while improving semantic consistency. Furthermore, an adaptive aggregation mechanism based on Kullback-Leibler (KL) divergence dynamically adjusts client weights to enhance robustness under heterogeneous data distributions. Experimental results demonstrate that the proposed method achieves excellent recommendation accuracy (average Hit@5 of 0.676 ± 0.009), sequence consistency (average NDCG@10 of 0.712 ± 0.008), and personalized responsiveness (average personalized score difference rate of 0. 38). The framework effectively balances global generalization and local adaptation while maintaining privacy protection, providing a feasible solution for secure and intelligent recommendation in distributed learning environments and offering technical insights for collaborative intelligence in privacy-sensitive electromagnetic information systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 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 License 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).
References
D. Gm, R. H. Goudar, A. A. Kulkarni, V. N. Rathod, and G. S. Hukkeri, “A digital recommendation system for personalized learning to enhance online education: A review,” IEEE Access, vol. 12, pp. 34019-34041, 2024, doi: 10.1109/ACCESS.2024.3369901.
A. Ellikkal and S. Rajamohan, “AI-enabled personalized learning: Empowering management students for improving engagement and academic performance,” Vilakshan-XIMB Journal of Management, vol. 22, no. 1, pp. 28-44, 2025, doi: 10.1108/XJM-02-2024-0023.
P. K. Myakala, A. K. Jonnalagadda, and C. Bura, “Federated learning and data privacy: A review of challenges and opportunities,” International Journal of Research Publication and Reviews, vol. 5, no. 12, Art. no. 10.55248, 2024, [Online]. Available: https://ssrn.com/abstract=5086425.
J. Fan, H. Lian, and W. Liu, “Privacy-preserving AI analytics in cloud computing: A federated learning approach for cross-organizational data collaboration,” Spectrum of Research, vol. 4, no. 2, pp. 1-21, 2024, [Online]. Available: http://spectrumofresearch.com/index.php/sr/article/view/15.
T. Z. Sana, S. Abdulla, A. Das, A. Nag, M. M. Hassan, Z. Z. Fiza, et al., “Advancing Federated Learning: A Systematic Literature Review of Methods, Challenges, and Applications,” IEEE Access, vol. 13, pp. 153817-153844, 2025, doi: 10.1109/ACCESS.2025.3605165.
Z. Zhang, Y. Zhang, D. Guo, S. Zhao, and X. Zhu, “Communication-efficient federated continual learning for distributed learning system with Non-IID data,” Science China Information Sciences, vol. 66, no. 2, Art. no. 122102, 2023, doi: 10.1007/s11432-020-3419-4.
L. Yang, J. Huang, W. Lin, and J. Cao, “Personalized federated learning on non-IID data via group-based metalearning,” ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 4, pp. 1-20, 2023, doi: 10.1145/3558005.
L. Jiang, K. Liu, Y. Wang, D. Wang, P. Wang, and Fu Y et al, “Reinforced explainable knowledge concept recommendation in MOOCs,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 3, pp. 1-20, 2023, doi: 10.1145/3579991.
J. W. Tzeng, N. F. Huang, Y. H. Chen, T. W. Huang, and Y. S. Su, “Personal learning material recommendation system for MOOCs based on the LSTM neural network,” Educational Technology & Society, vol. 27, no. 2, pp. 25-42, 2024, [Online]. Available: https://www.jstor.org/stable/48766161.
R. Gu, “Personalized learning path based on graph attention mechanism deep reinforcement learning research on recommender systems,” Journal of Computational Methods in Sciences and Engineering, vol. 25, no. 3, pp. 2411-2426, 2025, doi: 10.1177/14727978241313260.
S. Amin, M. I. Uddin, A. A. Alarood, W. K. Mashwani, A. O. Alzahrani, and H. A. Alzahrani, “An adaptable and personalized framework for top-N course recommendations in online learning,” Scientific Reports, vol. 14, no. 1, Art. no. 10382, 2024, doi: 10.1038/s41598-024-56497-1.
T. Hussain, L. Yu, M. Asim, A. Ahmed, and M. A. Wani, “Enhancing e-learning adaptability with automated learning style identification and sentiment analysis: A hybrid deep learning approach for smart education,” Information, vol. 15, no. 5, pp. 277, 2024, doi: 10.3390/info15050277.
A. A. Nemati, M. H. Sadreddini, and M. Mahdizade, “Comparative Federated Algorithms for Solving Non-IID Data Challenges,” Risk Assessment and Management Decisions, vol. 1, no. 2, pp. 277-283, 2024, doi: 10.48314/ramd.v1i2.56.
Y. Xu, Y. Zhu, Z. Wang, H. Xu, and Y. Liao, “Enhancing Federated Learning Through Layer-Wise Aggregation Over Non-IID Data,” IEEE Transactions on Services Computing, vol. 18, no. 2, pp. 798-811, 2025, doi: 10.1109/TSC.2025.3536309.
E. Yu, Z. Ye, Z. Zhang, L. Qian, and M. Xie, “A federated recommendation algorithm based on user clustering and meta-learning,” Applied Soft Computing, vol. 158, Art. no. 111483, 2024, doi: 10.1016/j.asoc.2024.111483.
A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” IEEE transactions on neural networks and learning systems, vol. 34, no. 12, pp. 9587-9603, 2022, doi: 10.1109/TNNLS.2022.3160699.
N. Singh, J. Rupchandani, and M. Adhikari, “Personalized federated learning for heterogeneous edge device: Self-knowledge distillation approach,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 4625-4632, 2023, doi: 10.1109/TCE.2023.3327757.
Y. Wang, S. Guo, D. Qiao, G. Liu, and M. Li, “Fedsg: A personalized subgraph federated learning framework on multiple non-iid graphs,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 5, pp. 3678-3690, 2024, doi: 10.1109/TETCI.2024.3372381.
G. Dai and J. Tang, “A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning,” Sensors, vol. 25, no. 3, pp. 967, 2025, doi: 10.3390/s25030967.
A. Belhadi, Y. Djenouri, F. A. de Alcantara Andrade, and G. Srivastava, “Federated constrastive learning and visual transformers for personal recommendation,” Cognitive Computation, vol. 16, no. 5, pp. 2551-2565, 2024, doi: 10.1007/s12559-024-10286-0.
Z. Li, Z. Zhong, P. Zuo, and H. Zhao, “A personalized federated learning method based on the residual multi-head attention mechanism,” Journal of King Saud University-Computer and Information Sciences, vol. 36, no. 4, Art. no. 102043, 2024, doi: 10.1016/j.jksuci.2024.102043.
S. Jiang, M. Lu, K. Hu, J. Wu, Y. Li, L. Weng, et al., “Personalized federated learning based on multi-head attention algorithm,” International Journal of Machine Learning and Cybernetics, vol. 14, no. 11, pp. 3783-3798, 2023, doi: 10.1007/s13042-023-01864-z.
Y. Jiang, X. Zhao, H. Li, Xue, and Y, “A Personalized Federated Learning Method Based on Knowledge Distillation and Differential Privacy,” Electronics, vol. 13, no. 17, pp. 3538, 2024, doi: 10.3390/electronics13173538.
Z. W. Tang, S. W. Xu, H. Jin, S. Liu, R. Zhai, and K. Lu, “Personalized federated learning via decoupling self-knowledge distillation and global adaptive aggregation,” Multimedia Systems, vol. 31, no. 2, pp. 1-20, 2025, doi: 10.1007/s00530-025-01719-3.
E. Ardic and Y. Genc, “Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy,” IEEE Access, vol. 13, pp. 54322-54337, 2025, doi: 10.1109/ACCESS.2025.3554138.
Y. Liu, S. Li, W. Li, H. Qian, and H. Xia, “A Personalized Federated Learning Algorithm Based on Dynamic Weight Allocation,” Electronics, vol. 14, no. 3, pp. 484, 2025, doi: 10.3390/electronics14030484.
X. Han, Q. Zhang, Z. He, and Z. Cai, “Confidence-based similarity-aware personalized federated learning for autonomous IoT,” IEEE Internet of Things Journal, vol. 11, no. 7, pp. 13070-13081, 2023, doi: 10.1109/JIOT.2023.3337520.
Z. Tan, J. Le, F. Yang, M. Huang, T. Xiang, and X. Liao, “Secure and accurate personalized federated learning with similarity-based model aggregation,” IEEE Transactions on Sustainable Computing, vol. 10, no. 1, pp. 132-145, 2024, doi: 10.1109/TSUSC.2024.3403427.
X. Wu, L. Xu, and L. Zhu, “Local differential privacy-based federated learning under personalized settings,” Applied Sciences, vol. 13, no. 7, pp. 4168, 2023, doi: 10.3390/app13074168.
X. Yang, W. Huang, and M. Ye, “Dynamic personalized federated learning with adaptive differential privacy,” Advances in Neural Information Processing Systems, vol. 36, pp. 72181-72192, 2023, [Online]. Available: https://github.com/xiyuanyang45/DynamicPFL.