FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing
It addresses efficiency and privacy issues for scalable federated recommendation systems, representing an incremental improvement over existing methods.
The paper tackled slow convergence and privacy risks in GNN-based federated recommendation systems by proposing FastPFRec, which achieved 32.0% fewer training rounds, 34.1% shorter training time, and 8.1% higher accuracy on real-world datasets.
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.