Abstract
Online education has become a new growth point, providing a massive worldwide learning opportunity for college students and workers. The recommendation system is provided to help users choose suitable courses wisely to satisfy their learning needs better. However, the traditional recommendation methods are more inclined to recommend a series of courses to users and ignore the user’s interest in knowledge concepts, and conventional centralized training algorithms risk data leakage. In this paper, we propose a federated framework FedAttn that can train model using decentralized data and aggregate gradient using an attention-based method. Additionally, we conduct experiments utilizing real-world datasets to validate that our approach can achieve a decent result and effectively protect user privacy.
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Qin, Y., Zhu, J., Huang, J. (2023). Privacy-Preserving Federated Learning Framework in Knowledge Concept Recommendation. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_33
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DOI: https://doi.org/10.1007/978-981-99-2356-4_33
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