mlsanityData to 14 Oct 2026

Co-clustering for Federated Recommender System

arXiv 2411.01690 ↗First posted 3 Nov 2024cs.IR cs.LGcited by 9

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Abstract (arXiv)

As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. However, the presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges. To address this issue and maximize the benefit of collaborative filtering (CF) in FRS, it is intuitive to consider clustering clients (users) as well as items into different groups and learning group-specific models. Existing methods either resort to client clustering via user representations-risking privacy leakage, or employ classical clustering strategies on item embeddings or gradients, which we found are plagued by the curse of dimensionality. In this paper, we delve into the inefficiencies of the K-Means method in client grouping, attributing failures due to the high dimensionality as well as data sparsity occurring in FRS, and propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism, to address clients heterogeneity and enhance the collaborative filtering within the federated framework. Specifically, the server initially formulates an item membership from the client-provided item networks. Subsequently, clients are grouped regarding a specific item category picked from the item membership during each communication round, resulting in an intelligently aggregated group model. Meanwhile, to comprehensively capture the global inter-relationships among items, we incorporate an additional supervised contrastive learning term based on the server-side generated item membership into the local training phase for each client. Extensive experiments on four datasets are provided, which verify the effectiveness of the proposed CoFedRec.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 32 of its 72 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

32 of 72 references.

References · 32

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20 more
  1. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  3. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  4. 2019Secure Federated Matrix Factorizationcited by 102
  5. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  6. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 90
  7. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  8. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  9. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  10. 2019Meta Matrix Factorization for Federated Rating Predictionscited by 48
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  14. 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 25
  15. 2022FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Samplingcited by 22
  16. 2023HeteFedRec: Federated Recommender Systems with Model Heterogeneitycited by 20
  17. 2023Dual Personalization on Federated Recommendationcited by 19
  18. 2023Vertical Federated Graph Neural Network for Recommender Systemcited by 17
  19. 2020FedeRank: User Controlled Feedback with Federated Recommender Systemscited by 7
  20. 2022Cali3F: Calibrated Fast Fair Federated Recommendation Systemcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 4.

9 papers.

  1. 2024PyG-SSL: A Graph Self-Supervised Learning Toolkitcited by 14
  2. 2025Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Itemscited by 2
  3. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  4. 2025Federated Continual Recommendationcited by 1
  5. 2025Graph Federated Learning for Personalized Privacy Recommendationcited by 1
  6. 2026Stitch the Fragments: One-Shot Hierarchical Federated Clusteringcited by 0
  7. 2026Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendationcited by 0
  8. 2026Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendationcited by 0
  9. 2026Personalized and Multi-View Representation for Federated Cold-Start Recommendationcited by 0