Secure Federated Matrix Factorization

By Di Chai, Leye Wang, Kai Chen, Qiang Yang. IEEE Intelligent Systems, 2019

457 citations recorded by the source corpus.

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 article, 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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/79bd2319c53eaa92c456ede0da22942f4197bfd7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fangzhao Wu
2. Chuhan Wu
3. Yongfeng Huang
4. Tao Qi
5. Xing Xie
6. Qinbin Li
7. Bingsheng He
8. Zhaomin Wu

Citations in the corpus

• Federated Multi-view Matrix Factorization for Personalized Recommendations (2020, methodology): “We used Federated Collaborative Filter (FCF) [13, 20, 21] as a baseline method.”
• FedRec: Privacy-Preserving News Recommendation with Federated Learning (2020, methodology): “There are also some explorations in applying federated learning to recommendation (Ammad et al., 2019; Chai et al., 2019).”
• Robust Federated Recommendation System (2020, methodology): “[10] Di Chai, Leye Wang, Kai Chen, and Qiang Yang.”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “also shows promising results in recommender system [12], [27], [207], natural language processing [65], etc.”
• Federated Learning for Healthcare Informatics (2019, background): “ly homomorphic encryption to multi-party computation (MPC) with neural networks such that the accuracy is almost lossless and only minimal modifications to the neural networks is required. Chai et al. [18] propose a secure matrix factorization framework under the federated learning setting, where the”
• A federated graph neural network framework for privacy-preserving personalization (2021, methodology): “To solve this problem, Chai et al. [3] proposed a federated matrix factorization (FedMF) method, where the item embeddings are protected by homomorphic encryption techniques.”