6卷 第8期
Hainan University, Haikou, Hainan, 570228, China
纸质出版：2025-08-30
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(2025). A Review of Triangular Core Problem of Recommendation System Based on Federated Learning. 科技理论与实践（英文版）, 6(8), 147-152.
Xiao Liu.A Review of Triangular Core Problem of Recommendation System Based on Federated Learning[J].Theory and Practice of Science and Technology,2025,06(08):147-152.
(2025). A Review of Triangular Core Problem of Recommendation System Based on Federated Learning. 科技理论与实践（英文版）, 6(8), 147-152. DOI： 10.47297/taposatWSP2633-456927.20250608.
Xiao Liu.A Review of Triangular Core Problem of Recommendation System Based on Federated Learning[J].Theory and Practice of Science and Technology,2025,06(08):147-152. DOI： 10.47297/taposatWSP2633-456927.20250608.
Traditional recommender systems face severe challenges of data privacy protection. Centralized data storage and processing mode can not meet the compliance requirements of General Data Protection Regulation (GDPR)
and there is a significant risk of privacy leakage. Federated learning provides a new solution for recommender systems through the distributed paradigm of "data does not move
model moves"
and federated recommender systems emerge. It is found that the federal recommendation system still faces many challenges in heterogeneity
privacy protection security and system efficiency
and the existing research has not fully revealed the internal relationship between these problems. This paper innovatively constructs a "privacy-effectiveness-efficiency" triangular equilibrium analysis framework
uses systematic literature analysis methods
focuses on analyzing the core problems and internal relations of privacy
effectiveness and efficiency of federated recommendation systems
systematically summarizes the progress and shortcomings of current research
and proposes optimization directions
providing theoretical references for subsequent research.
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