mlsanityData to 7 Oct 2026

Practical and Secure Federated Recommendation with Personalized Masks

arXiv 2109.02464 ↗First posted 18 Aug 2021cs.IR cs.AI cs.CRcited by 7

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Abstract (arXiv)

Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfuscation methods to protect the original ratings from leakage. However, the former comes with extra communication and computation costs, and the latter damages model accuracy. Neither of them could simultaneously satisfy the real-time feedback and accurate personalization requirements of recommender systems. In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness. In more details, we introduce the new idea of personalized mask generated only from local data and apply it in FedMMF. On the one hand, personalized mask offers protection for participants' private data without effectiveness loss. On the other hand, combined with the adaptive secure aggregation protocol, personalized mask could further improve efficiency. Theoretically, we provide security analysis for personalized mask. Empirically, we also show the superiority of the designed model on different real-world data sets.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

21 of 56 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2021Towards Personalized Federated Learningcited by 462
  9. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  10. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  11. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  12. 2020Survey of Personalization Techniques for Federated Learningcited by 144
9 more
  1. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  2. 2019Secure Federated Matrix Factorizationcited by 102
  3. 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
  4. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  5. 2019Private Federated Learning with Domain Adaptationcited by 36
  6. 2020TextHide: Tackling Data Privacy in Language Understanding Taskscited by 32
  7. 2020Federated Residual Learningcited by 23
  8. 2020Specialized federated learning using a mixture of expertscited by 23
  9. 2020Is Private Learning Possible with Instance Encoding?cited by 22

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 2.

7 papers.

  1. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  2. 2023Dual Personalization on Federated Recommendationcited by 19
  3. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  4. 2023FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendationcited by 4
  5. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1
  6. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  7. 2025Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendationcited by 0