# Secure Federated Matrix Factorization

arXiv: https://arxiv.org/abs/1906.05108 · HTML: https://mlsanity.com/paper/1906.05108/

## Key facts

- Cited by 102 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 102)

- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Towards Federated Bayesian Network Structure Learning with Continuous Optimization](https://mlsanity.com/paper/2110.09356/) (2021)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation](https://mlsanity.com/paper/2310.11730/) (2023)
- [No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://mlsanity.com/paper/2312.10080/) (2023)
- [Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation](https://mlsanity.com/paper/2202.07253/) (2022)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [A Survey for Federated Learning Evaluations: Goals and Measures](https://mlsanity.com/paper/2308.11841/) (2023)
- [Federated Recommendation via Hybrid Retrieval Augmented Generation](https://mlsanity.com/paper/2403.04256/) (2024)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training](https://mlsanity.com/paper/2401.03748/) (2024)
- [Deep Efficient Private Neighbor Generation for Subgraph Federated Learning](https://mlsanity.com/paper/2401.04336/) (2024)
- [FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence](https://mlsanity.com/paper/2112.11134/) (2021)
- [Federated Causality Learning with Explainable Adaptive Optimization](https://mlsanity.com/paper/2312.05540/) (2023)
- [A Payload Optimization Method for Federated Recommender Systems](https://mlsanity.com/paper/2107.13078/) (2021)
- [RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation](https://mlsanity.com/paper/2405.15182/) (2024)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Federated Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2410.08249/) (2024)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [Turning Privacy-preserving Mechanisms against Federated Learning](https://mlsanity.com/paper/2305.05355/) (2023)

## 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.