mlsanityData to 7 Oct 2026

A Survey on Federated Recommendation Systems

arXiv 2301.00767 ↗First posted 27 Dec 2022cs.IR cs.AI cs.CRcited by 25

Not on the map. Why?

Abstract (arXiv)

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 55 of its 117 references that are in the map. First posted on arXiv 27 Dec 2022.

Builds on

55 of 117 references.

References · 55

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2017Neural Collaborative Filteringcited by 1,108
  8. 2019Deep Leakage from Gradientscited by 1,004
  9. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  12. 2019A Closer Look at Few-shot Classificationcited by 679
38 more
  1. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  2. 2020Hard Negative Mixing for Contrastive Learningcited by 399
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  4. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  5. 2019Asynchronous Federated Optimizationcited by 264
  6. 2019Can You Really Backdoor Federated Learning?cited by 250
  7. 2014Compressing Deep Convolutional Networks using Vector Quantizationcited by 189
  8. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  9. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  10. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  11. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 135
  12. 2019One-Shot Federated Learningcited by 124
  13. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  14. 2019Secure Federated Matrix Factorizationcited by 102
  15. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  16. 2016Data Poisoning Attacks on Factorization-Based Collaborative Filteringcited by 78
  17. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  18. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
  19. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  20. 2018Poisoning Attacks to Graph-Based Recommender Systemscited by 52
  21. 2020Understanding Training Efficiency of Deep Learning Recommendation Models at Scalecited by 43
  22. 2021Data Poisoning Attacks to Deep Learning Based Recommender Systemscited by 41
  23. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  24. 2021Fast-adapting and Privacy-preserving Federated Recommender Systemcited by 30
  25. 2021Federated Neural Collaborative Filteringcited by 30
  26. 2020Practical Data Poisoning Attack against Next-Item Recommendationcited by 27
  27. 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 24
  28. 2021Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendationcited by 21
  29. 2022FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Samplingcited by 21
  30. 2022FedRecAttack: Model Poisoning Attack to Federated Recommendationcited by 18
  31. 2022ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferencescited by 15
  32. 2020A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendationscited by 13
  33. 2022Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarioscited by 13
  34. 2020Secure Social Recommendation based on Secret Sharingcited by 12
  35. 2020How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learningcited by 12
  36. 2021FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influencecited by 11
  37. 2021A Payload Optimization Method for Federated Recommender Systemscited by 10
  38. 2020Robust Federated Recommendation Systemcited by 8

50 most cited of 55 All 55 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 7; 2025: 11; 2026: 2.

25 papers.

Sort:

By area:

  1. 2024Poisoning Federated Recommender Systems with Fake Userscited by 15
  2. 2023Federated Recommendation with Additive Personalizationcited by 9
  3. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  4. 2024Efficient and Robust Regularized Federated Recommendationcited by 4
  5. 2025Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspectivecited by 4
  6. 2025A Systematic Survey on Federated Sequential Recommendationcited by 4
  7. 2024FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learningcited by 3
  8. 2024Federated Communication-Efficient Multi-Objective Optimizationcited by 3
  9. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  10. 2023Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clientscited by 1
  11. 2023RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulationcited by 1
  12. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
13 more
  1. 2025Federated Continual Recommendationcited by 1
  2. 2025Graph Federated Learning for Personalized Privacy Recommendationcited by 1
  3. 2025Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbackscited by 1
  4. 2023FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systemscited by 0
  5. 2024A Survey of Latent Factor Models in Recommender Systemscited by 0
  6. 2024Navigating the Future of Federated Recommendation Systems with Foundation Modelscited by 0
  7. 2024Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labelscited by 0
  8. 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learningcited by 0
  9. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  10. 2025Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendationcited by 0
  11. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  12. 2026Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendationcited by 0
  13. 2026Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendationcited by 0