Cluster-Enhanced Federated Graph Neural Network for Recommendation

• URL: http://arxiv.org/abs/2412.08066v2
• Date: Sat, 28 Dec 2024 06:27:42 GMT
• Title: Cluster-Enhanced Federated Graph Neural Network for Recommendation
• Authors: Haiyan Wang, Ye Yuan,
• Abstract summary: 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.
• Score: 8.92054926432611
• License:
• Abstract: Personal interaction data can be effectively modeled as individual graphs for each user in recommender systems.Graph Neural Networks (GNNs)-based recommendation techniques have become extremely popular since they can capture high-order collaborative signals between users and items by aggregating the individual graph into a global interactive graph.However, this centralized approach inherently poses a threat to user privacy and security. Recently, federated GNN-based recommendation techniques have emerged as a promising solution to mitigate privacy concerns. Nevertheless, current implementations either limit on-device training to an unaccompanied individual graphs or necessitate reliance on an extra third-party server to touch other individual graphs, which also increases the risk of privacy leakage. To address this challenge, we propose a Cluster-enhanced Federated Graph Neural Network framework for Recommendation, named CFedGR, which introduces high-order collaborative signals to augment individual graphs in a privacy preserving manner. Specifically, the server clusters the pretrained user representations to identify high-order collaborative signals. In addition, two efficient strategies are devised to reduce communication between devices and the server. Extensive experiments on three benchmark datasets validate the effectiveness of our proposed methods.

Related papers

• Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation [20.774848093888615]
  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.
  arXiv  Detail & Related papers  (2024-12-02T05:31:22Z)
• 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)
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement [20.214339212091012]
  Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recent Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. We propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information.
  arXiv  Detail & Related papers  (2024-01-20T02:38:21Z)
• Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems [5.465420718331109]
  Graph-based collaborative filtering methods have prevailing performance for recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information.
  arXiv  Detail & Related papers  (2023-11-30T11:49:33Z)
• 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)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)

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.