FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing
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 Gowall...
| Authors | Zhenxing Yan, Jidong Yuan, Yongqi Sun et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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