Clustered Federated Learning for Recommendation Systems: Tackling Data Heterogeneity

Yiyang Lyu

doi:10.54097/152yfh76

Authors

Yiyang Lyu School of Data Science, The Chinese University of Hong Kong, Shenzhen, China

DOI:

https://doi.org/10.54097/152yfh76

Keywords:

Clustered federated learning, recommendation system, data heterogeneity.

Abstract

Recommendation systems have shifted to the federated paradigm (FedRS) to address privacy risks and data silos, yet non-IID data heterogeneity is still a bottleneck limiting FedRS performance. And Clustered Federated Learning (CFL) emerges as a potential solution for recommendation systems. This review attempts to sort out two core CFL frameworks—Iterative Federated Clustering Algorithm (IFCA, alternating cluster identity estimation and model optimization) and SnapCFL (decoupling pre-clustering and dynamic client selection)—and three CFL-based FedRS approaches: user clustering (PerFedRec, FedGWC, FedPCL), item clustering (CoFedRec, ClusterGCF), and user-item co-clustering (CdFed, CPF-GCN), while analyzing challenges like dynamic user preferences, data sparsity/cold-start, and large-system scalability. With the rapid growth of personalized services and increasing privacy concerns, understanding how CFL can enhance model personalization and efficiency has become crucial for advancing privacy-preserving recommender systems. This review tries to present key progress in this field, hoping to provide some reference for readers to grasp the current research status and clarify subsequent directions.

Downloads

Download data is not yet available.

References

[1] Yang L, Tan B, Zheng VW, Chen K, Yang Q. Federated recommendation systems. In: Federated Learning: Privacy and Incentive. Cham: Springer International Publishing; 2020. p. 225 – 39.

[2] Duan M, Liu D, Ji X, Wu Y, Liang L, Chen X, et al. Flexible clustered federated learning for client-level data distribution shift. IEEE Trans Parallel Distrib Syst. 2022; 33 (11): 2661 – 74.

[3] Ghosh A, Chung J, Yin D, Ramchandran K. An efficient framework for clustered federated learning. Adv Neural Inf Process Syst. 2020; 33: 19586 – 97.

[4] Cheng Y, Zhang W, Zhang Z, Kang J, Xu Q, Wang S, et al. SnapCFL: A pre-clustering-based clustered federated learning framework for data and system heterogeneities. IEEE Trans Mobile Comput. 2025.

[5] 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 and Knowledge Management. 2022. p. 4289 – 93.

[6] Licciardi A, Leo D, Fanì E, Caputo B, Ciccone M. Interaction-aware Gaussian weighting for clustered federated learning. arXiv. 2025. arXiv: 2502.03340.

[7] Wang S, Zhou Y, Fan X, Li J, Lei Z, Gong M. Personalized federated contrastive learning for recommendation. IEEE Trans Comput Soc Syst. 2025.

[8] He X, Liu S, Keung J, He J. Co-clustering for federated recommender system. In: Proceedings of the ACM Web Conference 2024. 2024. p. 3821 – 32.

[9] Liu F, Zhao S, Cheng Z, Nie L, Kankan Halli M. Cluster-based graph collaborative filtering. ACM Trans Inf Syst. 2024; 42 (6): 1 – 24.

[10] Zhang R, Chen Y, Wu C, Wang F. Cluster-driven GNN-based federated recommendation with biased message dropout. In: 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE; 2023. p. 594 – 9.

[11] Mao X, Liu Y, Qi L, Duan L, Xu X, Zhang X, et al. Cluster-driven personalized federated recommendation with interest-aware graph convolution network for multimedia. In: Proceedings of the 32nd ACM International Conference on Multimedia. 2024. p. 5614 – 22.