A Novel Privacy-Preserved Recommender System Framework based on Federated Learning

Abstract

Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.

References in corpus (8)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Challenges, Methods, and Future Directions
• How To Backdoor Federated Learning
• On First-Order Meta-Learning Algorithms
• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
• Federated Learning with Bayesian Differential Privacy
• When being Weak is Brave: Privacy in Recommender Systems
• Privacy-Preserving News Recommendation Model Learning