提升联邦推荐效率与安全，减少训练轮次并防止数据泄露
FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing
- 本地更新策略加速收敛，隐私感知参数共享降低泄露风险
- 四组真实数据集测试中，训练轮次减少32.0%，时间缩短34.1%，准确率提升8.1%
- 适合需要高效且安全推荐系统的工业场景，如跨平台个性化服务
基于图神经网络的联邦推荐系统在保护数据隐私的同时有效捕捉用户-项目关系。然而，现有方法在图数据上常面临收敛缓慢和协作过程中的隐私泄露风险。为此，我们提出FastPFRec（快速个性化联邦推荐与安全共享），一种新型框架，在提升训练效率的同时增强数据安全性。FastPFRec通过高效的本地更新策略加速模型收敛，并引入隐私感知的参数共享机制以缓解泄露风险。在Yelp、Kindle、Gowalla-100k和Gowalla-1m四个真实世界数据集上的实验表明，相比现有基线方法，FastPFRec实现32.0%更少的训练轮次、34.1%更短的训练时间，且准确率提高8.1%。结果证明，FastPFRec为可扩展的联邦推荐提供了高效且隐私保护的解决方案。
原文摘要 · Abstract (English)
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.
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