Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy

• URL: http://arxiv.org/abs/2408.05160v2
• Date: Mon, 25 Nov 2024 05:43:10 GMT
• Title: Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy
• Authors: Linfeng Luo, Fengxiao Tang, Xiyu Liu, Zhiqi Guo, Zihao Qiu, Ming Zhao,
• Abstract summary: FedHGL is designed to collaboratively train a comprehensive hypergraph neural network across multiple clients. Cross-client feature aggregation is performed and distributed at the central server to ensure that this information can be utilized by the clients.
• Score: 6.295242666794106
• License:
• Abstract: As the volume and complexity increase, graph-structured data commonly need to be split and stored across distributed systems. To enable data mining on subgraphs within these distributed systems, federated graph learning has been proposed, allowing collaborative training of Graph Neural Networks (GNNs) across clients without sharing raw node features. However, when dealing with graph structures that involve high-order relationships between nodes, known as hypergraphs, existing federated graph learning methods are less effective. In this study, we introduce FedHGL, an innovative federated hypergraph learning algorithm. FedHGL is designed to collaboratively train a comprehensive hypergraph neural network across multiple clients, facilitating mining tasks on subgraphs of a hypergraph where relationships are not merely pairwise. To address the high-order information loss between subgraphs caused by distributed storage, we introduce a pre-propagation hyperedge completion operation before the federated training process. In this pre-propagation step, cross-client feature aggregation is performed and distributed at the central server to ensure that this information can be utilized by the clients. Furthermore, by incorporating local differential privacy (LDP) mechanisms, we ensure that the original node features are not disclosed during this aggregation process. Experimental results on seven real-world datasets confirm the effectiveness of our approach and demonstrate its performance advantages over traditional federated graph learning methods.

Related papers

• Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion [15.293684479404092]
  We propose a novel deep graph clustering method called CGCN. Our approach introduces contrastive signals and deep structural information into the pre-training process. Our method has been experimentally validated on multiple real-world graph datasets.
  arXiv  Detail & Related papers  (2024-08-08T09:49:26Z)
• Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network [13.786989442742588]
  Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients. We propose a graph convolutional neural network to capture feature-sharing information while learning features from a subset of clients. We also develop a simple but effective clustering algorithm that aggregates features produced by the deep neural networks of each client while preserving data privacy.
  arXiv  Detail & Related papers  (2024-04-15T04:02:39Z)
• Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks [9.987252149421982]
  We present a novel self-supervised pretraining framework for heterogeneous HyperGNNs. Our method is able to effectively capture higher-order relations among entities in the data in a self-supervised manner. Our experiments show that our proposed framework consistently outperforms state-of-the-art baselines in various downstream tasks.
  arXiv  Detail & Related papers  (2023-11-19T16:34:56Z)
• Distributed Learning over Networks with Graph-Attention-Based Personalization [49.90052709285814]
  We propose a graph-based personalized algorithm (GATTA) for distributed deep learning. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited.
  arXiv  Detail & Related papers  (2023-05-22T13:48:30Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Learnable Hypergraph Laplacian for Hypergraph Learning [34.28748027233654]
  HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. We propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD) HERALD adaptively optimize the adjacency relationship between hypernodes and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned.
  arXiv  Detail & Related papers  (2021-06-12T02:07:07Z)
• Learnable Hypergraph Laplacian for Hypergraph Learning [34.28748027233654]
  HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. We propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD) HERALD adaptively optimize the adjacency relationship between hypernodes and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned.
  arXiv  Detail & Related papers  (2021-06-10T12:37:55Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.