Secure Federated Matrix Factorization
This addresses privacy concerns for users in recommendation systems, but it is incremental as it builds on existing federated learning and encryption methods.
The paper tackles the problem of protecting user privacy in federated learning by proposing FedMF, a secure matrix factorization framework that uses homomorphic encryption to prevent data leakage from gradients, and demonstrates its feasibility on a real movie rating dataset.
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.