SIGN-FCF: Sign-based Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation

The integration of federated learning and recommendation systems is emerging as a prominent trend in machine learning, enabling personalized recommendations while preserving user privacy. In this paradigm, a master model is distributed to users, and the users perform local updates using their private data. The updates are sent back and aggregated on the server to update the master model then redistributed to the users. However, traditional federated recommendation systems encounter serveral challenges, including potential privacy leakage and high communication costs. To address these issues, we propose SIGN-FCF, a federated recommendation method based on matrix factorization, which leverages the SIGNSGD algorithm and differential privacy techniques. The proposed method employs a 1-bit compressor to enhance privacy protection and reduce communication costs, and three instances of the compressor are created to meet various privacy requirements. We also evaluate the performance of SIGN-FCF on three real-world datasets, demonstrating its effectiveness in preserving user privacy without compromising accuracy.