mlsanityData to 7 Oct 2026

Federated Recommendation with Additive Personalization

arXiv 2301.09109 ↗First posted 22 Jan 2023cs.LGcited by 9

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Abstract (arXiv)

Building recommendation systems via federated learning (FL) is a new emerging challenge for advancing next-generation Internet service and privacy protection. Existing approaches train shared item embedding by FL while keeping the user embedding private on client side. However, item embedding identical for all clients cannot capture users' individual differences on perceiving the same item and thus leads to poor personalization. Moreover, dense item embedding in FL results in expensive communication cost and latency. To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP enforces sparsity of the global view to save FL's communication cost and encourages difference between the two views through regularization. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for an user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 46 of its 90 references that are in the map. First posted on arXiv 22 Jan 2023.

Builds on

46 of 90 references.

References · 46

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  5. 2017Neural Collaborative Filteringcited by 1,108
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  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
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  10. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  11. 2019Federated Learning with Personalization Layerscited by 533
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34 more
  1. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
  2. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
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  4. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  5. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  6. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  7. 2022Federated Learning with Partial Model Personalizationcited by 114
  8. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  9. 2019Secure Federated Matrix Factorizationcited by 102
  10. 2020Collaborative Fairness in Federated Learningcited by 95
  11. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  12. 2018A General Approach to Adding Differential Privacy to Iterative Training Procedurescited by 84
  13. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
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  15. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
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  17. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  18. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  19. 2018Learning Private Neural Language Modeling with Attentive Aggregationcited by 49
  20. 2022Federated Unlearning for On-Device Recommendationcited by 39
  21. 2022On the Convergence of Clustered Federated Learningcited by 31
  22. 2022Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systemscited by 31
  23. 2021Fast-adapting and Privacy-preserving Federated Recommender Systemcited by 30
  24. 2021Federated Neural Collaborative Filteringcited by 30
  25. 2020Federated Multi-view Matrix Factorization for Personalized Recommendationscited by 27
  26. 2021Stronger Privacy for Federated Collaborative Filtering with Implicit Feedbackcited by 26
  27. 2022LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorizationcited by 26
  28. 2022A Survey on Federated Recommendation Systemscited by 25
  29. 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 24
  30. 2022FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Samplingcited by 21
  31. 2023Dual Personalization on Federated Recommendationcited by 19
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 4; 2026: 3.

9 papers.

  1. 2023RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulationcited by 1
  2. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  3. 2025Federated Causal Inference in Healthcare: Methods, Challenges, and Applicationscited by 1
  4. 2026Discrete Prototypical Memories for Federated Time Series Foundation Modelscited by 1
  5. 2024Personalized Additive Modeling for Multi-level Federated Learningcited by 0
  6. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  7. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  8. 2026Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendationcited by 0
  9. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0