mlsanityData to 7 Oct 2026

Secure Federated Matrix Factorization

arXiv 1906.05108 ↗First posted 12 Jun 2019cs.CR cs.LGcited by 102

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Abstract (arXiv)

To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data. In this paper, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users' raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 15; 2022: 14; 2023: 21; 2024: 21; 2025: 19; 2026: 12.

102 papers.

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  1. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  2. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  5. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  6. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  7. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  8. 2021Federated Neural Collaborative Filteringcited by 30
  9. 2022A Survey on Federated Recommendation Systemscited by 25
  10. 2023Semi-decentralized Federated Ego Graph Learning for Recommendationcited by 22
  11. 2023Dual Personalization on Federated Recommendationcited by 19
  12. 2023Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendationcited by 18
38 more
  1. 2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendationcited by 18
  2. 2022Exploiting Data Sparsity in Secure Cross-Platform Social Recommendationcited by 17
  3. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  4. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  5. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  6. 2024Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Trainingcited by 14
  7. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  8. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  9. 2021FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influencecited by 11
  10. 2023Federated Causality Learning with Explainable Adaptive Optimizationcited by 11
  11. 2021A Payload Optimization Method for Federated Recommender Systemscited by 10
  12. 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregationcited by 10
  13. 2023Federated Recommendation with Additive Personalizationcited by 9
  14. 2023Federated Analytics: A surveycited by 9
  15. 2024Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approachcited by 9
  16. 2024Federated Graph Learning for Cross-Domain Recommendationcited by 9
  17. 2024Co-clustering for Federated Recommender Systemcited by 9
  18. 2023Turning Privacy-preserving Mechanisms against Federated Learningcited by 8
  19. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  20. 2021Practical and Secure Federated Recommendation with Personalized Maskscited by 7
  21. 2023Differentially Private Federated Clustering over Non-IID Datacited by 7
  22. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  23. 2023User Consented Federated Recommender System Against Personalized Attribute Inference Attackcited by 7
  24. 2022FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendationcited by 6
  25. 2022Cali3F: Calibrated Fast Fair Federated Recommendation Systemcited by 5
  26. 2023Federated Nearest Neighbor Machine Translationcited by 5
  27. 2023FedJudge: Federated Legal Large Language Modelcited by 5
  28. 2024A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendationcited by 5
  29. 2021FedPower: Privacy-Preserving Distributed Eigenspace Estimationcited by 4
  30. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  31. 2022FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddingscited by 4
  32. 2023FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendationcited by 4
  33. 2024A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systemscited by 4
  34. 2024Efficient and Robust Regularized Federated Recommendationcited by 4
  35. 2025Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspectivecited by 4
  36. 2021HAFLO: GPU-Based Acceleration for Federated Logistic Regressioncited by 3
  37. 2022FedRule: Federated Rule Recommendation System with Graph Neural Networkscited by 3
  38. 2025Preventing the Popular Item Embedding Based Attack in Federated Recommendationscited by 3

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