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arXiv · 2412.01141

Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation

Guowei Wu·Weike Pan·Qiang Yang·Zhong Ming
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Abstract

Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. However, existing GNN-based recommendation methods rely on a centralized storage of fragmented user-item interaction sub-graphs and training on an aggregated global graph, which will lead to privacy concerns. As a response, some recent works develop GNN-based federated recommendation methods by exploiting decentralized and fragmented user-item sub-graphs in order to preserve user privacy. However, due to privacy constraints, the graph convolution process in existing federated recommendation methods is incomplete compared with the centralized counterpart, causing a degradation of the recommendation performance. In this paper, we propose a novel lossless and privacy-preserving graph convolution network (LP-GCN), which fully completes the graph convolution process with decentralized user-item interaction sub-graphs while ensuring privacy. It is worth mentioning that its performance is equivalent to that of the non-federated (i.e., centralized) counterpart. Moreover, we validate its effectiveness through both theoretical analysis and empirical studies. Extensive experiments on three real-world datasets show that our LP-GCN outperforms the existing federated recommendation methods. The code will be publicly available once the paper is accepted.

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Guowei Wu, Weike Pan, Qiang Yang, Zhong Ming. 2024-12-02. Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation. https://arxiv.org/abs/2412.01141

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