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

• URL: http://arxiv.org/abs/2412.01141v1
• Date: Mon, 02 Dec 2024 05:31:22 GMT
• Title: Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
• Authors: Guowei Wu, Weike Pan, Qiang Yang, Zhong Ming,
• Abstract summary: Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. Existing GNN-based recommendation methods rely on a centralized storage of fragmented user-item interaction sub-graphs and training on an aggregated global graph.
• Score: 20.774848093888615
• License:
• 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.

Related papers

• Cluster-Enhanced Federated Graph Neural Network for Recommendation [8.92054926432611]
  We propose a Cluster-enhanced Federated Graph Neural Network framework for Recommendation, named CFedGR. This framework introduces high-order collaborative signals to augment individual graphs in a privacy preserving manner. Two efficient strategies are devised to reduce communication between devices and the server.
  arXiv  Detail & Related papers  (2024-12-11T03:22:04Z)
• Scalable Graph Compressed Convolutions [68.85227170390864]
  We propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning.
  arXiv  Detail & Related papers  (2024-07-26T03:14:13Z)
• Cluster-based Graph Collaborative Filtering [55.929052969825825]
  Graph Convolution Networks (GCNs) have succeeded in learning user and item representations for recommendation systems. Most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. We propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF)
  arXiv  Detail & Related papers  (2024-04-16T07:05:16Z)
• GNN4FR: A Lossless GNN-based Federated Recommendation Framework [13.672867761388675]
  Graph neural networks (GNNs) have gained wide popularity in recommender systems. Our framework achieves full-graph training with complete high-order structure information. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence.
  arXiv  Detail & Related papers  (2023-07-25T16:55:17Z)
• Vertical Federated Graph Neural Network for Recommender System [1.7802147489386633]
  Graph neural network (GNN) is becoming a promising approach for federated recommender systems. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN.
  arXiv  Detail & Related papers  (2023-03-10T08:39:26Z)
• Semi-decentralized Federated Ego Graph Learning for Recommendation [58.21409625065663]
  We propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL. The proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques.
  arXiv  Detail & Related papers  (2023-02-10T03:57:45Z)
• Analyzing the Effect of Sampling in GNNs on Individual Fairness [79.28449844690566]
  Graph neural network (GNN) based methods have saturated the field of recommender systems. We extend an existing method for promoting individual fairness on graphs to support mini-batch, or sub-sample based, training of a GNN. We show that mini-batch training facilitate individual fairness promotion by allowing for local nuance to guide the process of fairness promotion in representation learning.
  arXiv  Detail & Related papers  (2022-09-08T16:20:25Z)
• An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering [79.17319280791237]
  We propose an adaptive graph pre-training framework for localized collaborative filtering (ADAPT) ADAPT captures both the common knowledge across different graphs and the uniqueness for each graph. It does not require transferring user/item embeddings, and is able to capture both the common knowledge across different graphs and the uniqueness for each graph.
  arXiv  Detail & Related papers  (2021-12-14T06:53:13Z)
• RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding [42.46797662323393]
  We develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF) RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive. We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2020-07-07T12:26:10Z)
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [100.76229017056181]
  Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation.
  arXiv  Detail & Related papers  (2020-02-06T06:53:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.