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

TL;DR

This paper introduces a privacy-preserving recommender system framework utilizing federated learning, enabling personalized recommendations without centralizing user data, thus enhancing privacy and regulatory compliance.

Contribution

It presents a novel federated learning-based framework for recommender systems that maintains user privacy while supporting various algorithms.

Findings

Reduces privacy leakage risk

Supports multiple recommendation algorithms

Ensures compliance with privacy regulations

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.