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GNN4FR: A Lossless GNN-based Federated Recommendation Framework

arXiv 2308.01197 ↗First posted 25 Jul 2023cs.IR cs.CR cs.LGcited by 0

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Abstract (arXiv)

Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data between a user and the corresponding items and then model them in a central server, which would break the privacy laws such as GDPR. So far, no existing work can construct a global graph without leaking each user's private interaction data (i.e., his or her subgraph). In this paper, we are the first to design a novel lossless federated recommendation framework based on GNN, which achieves full-graph training with complete high-order structure information, enabling the training process to be equivalent to the corresponding un-federated counterpart. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 25 Jul 2023.

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References · 14

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  3. 2019Graph Neural Networks for Social Recommendationcited by 714
  4. 2019Neural Graph Collaborative Filteringcited by 661
  5. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 343
  6. 2019Secure Federated Matrix Factorizationcited by 102
  7. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  8. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  9. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  10. 2019STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systemscited by 54
  11. 2020Federated Multi-view Matrix Factorization for Personalized Recommendationscited by 27
  12. 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 25
2 more
  1. 2023Semi-decentralized Federated Ego Graph Learning for Recommendationcited by 22
  2. 2022FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddingscited by 4

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