One Node Per User: Node-Level Federated Learning for Graph Neural Networks

• URL: http://arxiv.org/abs/2409.19513v1
• Date: Sun, 29 Sep 2024 02:16:07 GMT
• Title: One Node Per User: Node-Level Federated Learning for Graph Neural Networks
• Authors: Zhidong Gao, Yuanxiong Guo, Yanmin Gong,
• Abstract summary: We propose a novel framework for node-level federated graph learning. We introduce a graph Laplacian term based on the feature vector's latent representation to regulate the user-side model updates.
• Score: 7.428431479479646
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a solution, enabling collaborative model training without users directly sharing their raw data. However, integrating federated learning with GNNs presents unique challenges, especially when a client represents a graph node and holds merely a single feature vector. In this paper, we propose a novel framework for node-level federated graph learning. Specifically, we decouple the message-passing and feature vector transformation processes of the first GNN layer, allowing them to be executed separately on the user devices and the cloud server. Moreover, we introduce a graph Laplacian term based on the feature vector's latent representation to regulate the user-side model updates. The experiment results on multiple datasets show that our approach achieves better performance compared with baselines.

Related papers

• Federated Graph Learning with Graphless Clients [52.5629887481768]
  Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs) We propose a novel framework FedGLS to tackle the problem in FGL with graphless clients.
  arXiv  Detail & Related papers  (2024-11-13T06:54:05Z)
• A Unified Graph Selective Prompt Learning for Graph Neural Networks [20.595782116049428]
  Graph Prompt Feature (GPF) has achieved remarkable success in adapting pre-trained models for Graph Neural Networks (GNNs) We propose a new unified Graph Selective Prompt Feature learning (GSPF) for GNN fine-tuning.
  arXiv  Detail & Related papers  (2024-06-15T04:36:40Z)
• 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)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)
• 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)
• FedGraph: Federated Graph Learning with Intelligent Sampling [7.798227884125872]
  Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. Existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), which cannot efficiently handle graph data that are popular in many applications. In this paper, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph.
  arXiv  Detail & Related papers  (2021-11-02T04:58:03Z)
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks [59.378148590027735]
  We propose a graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores.
  arXiv  Detail & Related papers  (2021-01-06T03:05:37Z)
• GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs [18.23435958000212]
  Graph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Most existing GNN models exploit a single type of aggregator to aggregate neighboring nodes information. We propose a novel graph neural network architecture, Graph Attention & Interaction Network (GAIN), for inductive learning on graphs.
  arXiv  Detail & Related papers  (2020-11-03T00:20:24Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)

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.