FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing

TL;DR

FastPFRec is a federated recommendation framework that significantly improves training speed and privacy security using efficient local updates and privacy-aware sharing, validated on multiple real-world datasets.

Contribution

Introduces FastPFRec, a novel federated recommendation method that accelerates convergence and enhances privacy protection through innovative local update and parameter sharing strategies.

Findings

Reduces training rounds by 32%

Cuts training time by 34.1%

Improves accuracy by 8.1%

Abstract

Graph neural network (GNN)-based federated recommendation systems effectively capture user-item relationships while preserving data privacy. However, existing methods often face slow convergence on graph data and privacy leakage risks during collaboration. To address these challenges, we propose FastPFRec (Fast Personalized Federated Recommendation with Secure Sharing), a novel framework that enhances both training efficiency and data security. FastPFRec accelerates model convergence through an efficient local update strategy and introduces a privacy-aware parameter sharing mechanism to mitigate leakage risks. Experiments on four real-world datasets (Yelp, Kindle, Gowalla-100k, and Gowalla-1m) show that FastPFRec achieves 32.0% fewer training rounds, 34.1% shorter training time, and 8.1% higher accuracy compared with existing baselines. These results demonstrate that FastPFRec provides an efficient and privacy-preserving solution for scalable federated recommendation.