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FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing

Zhenxing Yan, Jidong Yuan, Yongqi Sun, Haiyang Liu, Zhihui Gao

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.

FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing

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.
Paper Structure (36 sections, 18 equations, 10 figures, 11 tables, 1 algorithm)

This paper contains 36 sections, 18 equations, 10 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. GNN-Based Federated Recommendations
  4. Privacy Protection in Federated Recommendation
  5. Method
  6. Problem Definition
  7. Client-Side Method
  8. User-Item Graph Construction as Input
  9. Client-side Privacy Protection Mechanism
  10. Message Passing in Local FastGNN
  11. Model Parameter Upload and Download
  12. Trusted Node
  13. Receiving Disturbed Parameters from Clients
  14. Uploading and Distributing
  15. Privacy Protection Mechanism of Trusted Node
  16. ...and 21 more sections

Figures (10)

  • Figure 1: An example of federated attack with noise injection.
  • Figure 2: Comparison of training NDCG across epochs on the Gowalla-1m dataset.
  • Figure 3: The proposed FastPFRec framework. This flowchart illustrates the end-to-end process, highlighting the local training with FastGNN on client devices and the subsequent aggregation of model updates by trusted nodes before being sent to the central server.
  • Figure 4: The position of trusted node in the overall framework.
  • Figure 5: CD diagram on HR.
  • ...and 5 more figures