Privacy-preserving federated learning framework in multimedia courses recommendation

Author:

Qin YangJieORCID,Li Ming,Zhu Jia

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Reference32 articles.

1. Ali, W., Kumar, R., Deng, Z., Wang, Y., & Shao, J. (2021). A federated learning approach for privacy protection in context-aware recommender systems. The Computer Journal.

2. Ammad-Ud-Din, M., Ivannikova, E., Khan, S. A., Oyomno, W., Fu, Q., Tan, K. E., & Flanagan, A. (2019). Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888.

3. Bennett, J., Lanning, S., et al. (2007). The netflix prize. In: Proceedings of KDD cup and workshop, 35.

4. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H. B., et al (2019). Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046.

5. Chai, D., Wang, L., Chen, K., & Yang, Q. (2020). Secure federated matrix factorization. IEEE Intelligent Systems.

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