# Practical and Secure Federated Recommendation with Personalized Masks

arXiv: https://arxiv.org/abs/2109.02464 · HTML: https://mlsanity.com/paper/2109.02464/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [Survey of Personalization Techniques for Federated Learning](https://mlsanity.com/paper/2003.08673/) (2020)
- [Lower Bounds and Optimal Algorithms for Personalized Federated Learning](https://mlsanity.com/paper/2010.02372/) (2020)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [InstaHide: Instance-hiding Schemes for Private Distributed Learning](https://mlsanity.com/paper/2010.02772/) (2020)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Private Federated Learning with Domain Adaptation](https://mlsanity.com/paper/1912.06733/) (2019)
- [TextHide: Tackling Data Privacy in Language Understanding Tasks](https://mlsanity.com/paper/2010.06053/) (2020)
- [Federated Residual Learning](https://mlsanity.com/paper/2003.12880/) (2020)
- [Specialized federated learning using a mixture of experts](https://mlsanity.com/paper/2010.02056/) (2020)
- [Is Private Learning Possible with Instance Encoding?](https://mlsanity.com/paper/2011.05315/) (2020)

## Built on by (most cited first, 7 of 7)

- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation](https://mlsanity.com/paper/2205.02359/) (2022)
- [FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation](https://mlsanity.com/paper/2305.06272/) (2023)
- [Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network](https://mlsanity.com/paper/2404.09443/) (2024)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)

## 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.