Abstract
Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users’ privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute their local collaborative information by uploading model parameters to a central server. While this rigid framework protects users’ raw data during training, it severely compromises the recommendation model’s performance due to the following reasons: (1) Due to the power law distribution nature of user behavior data, individual users have few data points to train a recommendation model, resulting in uploaded model updates that may be far from optimal; (2) As each user’s uploaded parameters are learned from local data, which lacks global collaborative information, relying solely on parameter aggregation methods such as FedAvg to fuse global collaborative information may be suboptimal. To bridge this performance gap, we propose a novel federated recommendation framework, PDC-FRS. Specifically, we design a privacy-preserving data contribution mechanism that allows users to share their data with a differential privacy guarantee. Based on the shared but perturbed data, an auxiliary model is trained in parallel with the original federated recommendation process. This auxiliary model enhances FedRec by augmenting each user’s local dataset and integrating global collaborative information. To demonstrate the effectiveness of PDC-FRS, we conduct extensive experiments on two widely used recommendation datasets. The empirical results showcase the superiority of PDC-FRScompared to baseline methods.
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Notes
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In this paper, we focus on cross-user federated recommendation, where each client represents one user. Therefore, the concepts of user and client are interchangeable.
- 2.
In this paper, we mainly focus on recommendation with implicit feedback.
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References
Ammad-Ud-Din, M., Ivannikova, E., Khan, S.A., Oyomno, W., Fu, Q., Tan, K.E., Flanagan, A.: Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019)
Anelli, V.W., Deldjoo, Y., Di Noia, T., Ferrara, A., Narducci, F.: FedeRank: User Controlled Feedback with Federated Recommender Systems. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12656, pp. 32–47. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72113-8_3
Arachchige, P.C.M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., Atiquzzaman, M.: Local differential privacy for deep learning. IEEE Internet Things J. 7(7), 5827–5842 (2019)
Chai, D., Wang, L., Chen, K., Yang, Q.: Secure federated matrix factorization. IEEE Intell. Syst. 36(5), 11–20 (2020)
Chen, L., Yuan, W., Chen, T., Ye, G., Hung, N.Q.V., Yin, H.: Adversarial item promotion on visually-aware recommender systems by guided diffusion. ACM Trans. Inf, Syst (2024)
Chen, Z., Wang, S.: A review on matrix completion for recommender systems. Knowl. Inf. Syst. 64(1), 1–34 (2022)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. pp. 191–198 (2016)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. pp. 639–648 (2020)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. pp. 173–182 (2017)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Krichene, W., Rendle, S.: On sampled metrics for item recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 1748–1757 (2020)
Li, T., Song, L., Fragouli, C.: Federated recommendation system via differential privacy. In: 2020 IEEE international symposium on information theory (ISIT). pp. 2592–2597. IEEE (2020)
Liang, F., Pan, W., Ming, Z.: Fedrec++: Lossless federated recommendation with explicit feedback. In: Proceedings of the AAAI conference on artificial intelligence. vol. 35, pp. 4224–4231 (2021)
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)
Luo, S., Xiao, Y., Song, L.: Personalized federated recommendation via joint representation learning, user clustering, and model adaptation. In: Proceedings of the 31st ACM international conference on information & knowledge management. pp. 4289–4293 (2022)
McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07). pp. 94–103. IEEE (2007)
Muhammad, K., Wang, Q., O’Reilly-Morgan, D., Tragos, E., Smyth, B., Hurley, N., Geraci, J., Lawlor, A.: Fedfast: Going beyond average for faster training of federated recommender systems. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1234–1242 (2020)
Qu, L., Yuan, W., Zheng, R., Cui, L., Shi, Y., Yin, H.: Towards personalized privacy: User-governed data contribution for federated recommendation. arXiv preprint arXiv:2401.17630 (2024)
Sun, Z., Xu, Y., Liu, Y., He, W., Jiang, Y., Wu, F., Cui, L.: A survey on federated recommendation systems. arXiv preprint arXiv:2301.00767 (2022)
Wang, Q., Yin, H., Chen, T., Yu, J., Zhou, A., Zhang, X.: Fast-adapting and privacy-preserving federated recommender system. VLDB J. 31(5), 877–896 (2022)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. pp. 165–174 (2019)
Wang, Z., Yu, J., Gao, M., Yuan, W., Ye, G., Sadiq, S., Yin, H.: Poisoning attacks and defenses in recommender systems: A survey (2024)
Wu, C., Wu, F., Cao, Y., Huang, Y., Xie, X.: Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021)
Yang, L., Tan, B., Zheng, V.W., Chen, K., Yang, Q.: Federated recommendation systems. Federated Learning: Privacy and Incentive pp. 225–239 (2020)
Yin, H., Cui, B.: Spatio-temporal recommendation in social media. Springer (2016)
Yin, H., Qu, L., Chen, T., Yuan, W., Zheng, R., Long, J., Xia, X., Shi, Y., Zhang, C.: On-device recommender systems: A comprehensive survey. arXiv preprint arXiv:2401.11441 (2024)
Yuan, W., Nguyen, Q.V.H., He, T., Chen, L., Yin, H.: Manipulating federated recommender systems: Poisoning with synthetic users and its countermeasures. arXiv preprint arXiv:2304.03054 (2023)
Yuan, W., Qu, L., Cui, L., Tong, Y., Zhou, X., Yin, H.: Hetefedrec: Federated recommender systems with model heterogeneity. arXiv preprint arXiv:2307.12810 (2023)
Yuan, W., Yang, C., Nguyen, Q.V.H., Cui, L., He, T., Yin, H.: Interaction-level membership inference attack against federated recommender systems. In: Proceedings of the ACM Web Conference 2023. pp. 1053–1062 (2023)
Yuan, W., Yang, C., Qu, L., Ye, G., Nguyen, Q.V.H., Yin, H.: Robust federated contrastive recommender system against model poisoning attack. arXiv preprint arXiv:2403.20107 (2024)
Yuan, W., Yin, H., Wu, F., Zhang, S., He, T., Wang, H.: Federated unlearning for on-device recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. pp. 393–401 (2023)
Yuan, W., Yuan, S., Zheng, K., Nguyen, Q.V.H., Yin, H.: Manipulating visually-aware federated recommender systems and its countermeasures. arXiv preprint arXiv:2305.08183 (2023)
Zaier, Z., Godin, R., Faucher, L.: Evaluating recommender systems. In: 2008 International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution. pp. 211–217. IEEE (2008)
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021)
Zhang, S., Yin, H., Chen, T., Huang, Z., Nguyen, Q.V.H., Cui, L.: Pipattack: Poisoning federated recommender systems for manipulating item promotion. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. pp. 1415–1423 (2022)
Zhang, S., Yuan, W., Yin, H.: Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE Transactions on Knowledge and Data Engineering (2023)
Zhang, Y., Ye, Q., Chen, R., Hu, H., Han, Q.: Trajectory data collection with local differential privacy. Proceedings of the VLDB Endowment 16(10), 2591–2604 (2023)
Zheng, R., Qu, L., Cui, B., Shi, Y., Yin, H.: Automl for deep recommender systems: A survey. ACM Transactions on Information Systems 41(4), 1–38 (2023)
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Yang, C., Yuan, W., Qu, L., Nguyen, T.T. (2025). PDC-FRS: Privacy-Preserving Data Contribution for Federated Recommender System. In: Sheng, Q.Z., et al. Advanced Data Mining and Applications. ADMA 2024. Lecture Notes in Computer Science(), vol 15392. Springer, Singapore. https://doi.org/10.1007/978-981-96-0850-8_5
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