FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing

2026-03-18

AI Summary

This work addresses the challenges of slow convergence and high privacy leakage risks in existing graph neural network–based federated recommendation systems. To overcome these limitations, we propose FastPFRec, a novel framework that integrates an efficient local update strategy with a privacy-aware parameter sharing mechanism, thereby accelerating model convergence while enhancing data security. Extensive experiments on four real-world datasets demonstrate that FastPFRec reduces the number of training rounds by 32.0% on average, shortens total training time by 34.1%, and improves recommendation accuracy by 8.1% compared to state-of-the-art methods, achieving superior performance across all evaluated metrics.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• federated recommendation
• slow convergence
• privacy leakage
• graph neural network
• data privacy

Innovation

• Federated Recommendation
• Graph Neural Network
• Privacy-Preserving
• Efficient Training
• Secure Parameter Sharing