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FedKGRec: privacy-preserving federated knowledge graph aware recommender system

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Abstract

Knowledge Graph(KG) aware recommendation generally incorporates KG as side information to enhance the user and item representations. Although effective in addressing data sparsity and cold start issues, these methods can raise privacy concerns and legal risks due to the centralized storage of user-item interactions. In order to solve this issue, a novel privacy-preserving framework for KG-aware recommendation named FedKGRec is introduced, which trains the recommendation model collaboratively with the orchestration of a central server. First, we design a local KG-aware Recommendation model (KGRec), crux of which are the user preference propagation module and the item neighbor expansion module, aiming to enhance the user and item representations simultaneously. Then, the local differential privacy (LDP) technique is applied to perturb the local model parameters before they are sent to the central server or aggregator, making it extremely difficult for malicious parties to extract individual sensitive information from the aggregated results. Extensive comparative experiments on three public datasets demonstrate that the proposed FedKGRec outperforms the state-of-the-art federated recommendation methods in terms of AUC, ACC and F1.

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Data availability and access

The data that support the findings of this study are openly available at https://grouplens.org/datasets/hetrec2011/, http://www2.informatik.uni-freiburg.de/~cziegler/BX/ and https://gro-uplens.org/datasets/movielens/1m/https://grouplens.org/datasets/movielens/1m/.

Notes

  1. https://grouplens.org/datasets/hetrec2011/

  2. http://www2.informatik.uni-freiburg.de/~cziegler/BX/

  3. https://grouplens.org/datasets/movielens/1m/

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Acknowledgements

We would like to express our deepest gratitude to those who have provided support and assistance in the completion of this manuscript. In addition, fundings from the foundations as follow are gratefully acknowledged: 1) the National Natural Science Foundation of China under Grant No. 62102159 and No. 61802440, 2) the Humanities and Social Science Fund of Ministry of Education of China under Grant No. 21YJC870002, 3) the Natural Science Foundation of Hubei Province under grant No.2024AFB957 and No.2023AFB1018, 4) Knowledge Innovation Program of Wuhan-Shuguang Project under Grant No. 2022010801020287, and 5) the Fundamental Research Funds for the Central Universities under Grant No. CCNU24ZZ148 and No. CCNU22QN017.

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Authors and Affiliations

  1. School of Information Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Xiao Ma, Hongyu Zhang & Xuan Wen

  2. School of Information Management, Central China Normal University, Wuhan, China

    Jiangfeng Zeng

  3. Center for Data Governance and Intelligent Decision-Making of Hubei Province, Wuhan, China

    Jiangfeng Zeng

  4. School of Information Management, Wuhan University, Wuhan, China

    Yiqi Duan

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  1. Xiao Ma
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Contributions

Xiao Ma: Conceptualization, Writing original draft and editing, Funding acquisition. Hongyu Zhang: Methodology, Coding, Writing original draft and editing. Jiangfeng Zeng: Project administration, Funding acquisition, Writing review. Yiqi Duan: Writing review. Xuan Wen: Writing review.

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Correspondence to Jiangfeng Zeng.

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Ma, X., Zhang, H., Zeng, J. et al. FedKGRec: privacy-preserving federated knowledge graph aware recommender system. Appl Intell 54, 9028–9044 (2024). https://doi.org/10.1007/s10489-024-05634-4

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