Graph Federated Learning (FL) has attracted extensive attention in recent years due to its ability to train global graph models in a distributed manner without exposing original local data. However, fine-grained data heterogeneity remains largely overlooked in collaborative graph model training. Existing graph FL methods that address heterogeneity are mostly adapted from traditional FL and fail to account for the unique complexity of graph-specific heterogeneity. Specifically, graph heterogeneity can be further decomposed into feature heterogeneity and structural heterogeneity, which are tightly coupled during local training. To address this issue, we propose a novel local graph module, Feature and Structure Decoupling Convolution (FSD-Conv), designed to disentangle the interplay between feature bias and structural bias. With FSD-Conv, clients can learn feature-related yet structure-unbiased representations, thereby alleviating the adverse impact of graph heterogeneity in federated training. Furthermore, we introduce FedFSD, a personalized graph FL framework that achieves effective personalized model aggregation through an explainable neural network operating in a low-dimensional space. Extensive experiments on six graph datasets under both disjoint and overlapping client partitioning schemes demonstrate the effectiveness of FedFSD in handling complex graph data heterogeneity.

Graph Contrastive Learning(GCL) is a fundamental pretraining research method in Graph Neural Networks (GNNs), which puts rich graph-level insights into the graph data to augment the data representation. However, since the existing GCLs generally regard the intra-layer node as negative samples, they cannot cope with the diverse coupled neighbor relationships, which can be pre-trained with the combination of negative and positive samples. Coupled relationships can keep the attribute preference in node-level contrast and correctly pass this preference into the downstream tasks. To further prove the effectiveness of coupled neighbor relationships in the pretraining phase, we propose a novel GNN pretraining model Neighbor Pair Contrastive Graph Siamese Networks (NPC-GSN) for graph contrast. NPC-GSN expands the dissimilar neighbor’s representation discrepancy and decreases the representation discrepancy of similar neighbors in the pretraining phase, aiming to promote downstream node classification. Our extensive experiments on five graph datasets against several pretraining GNN models demonstrate the competitive effectiveness of NPC-GSN in node classification, and the frequency domain and ablation experiments also verify the effectiveness of NPC-GSN.

Machine learning, particularly deep learning, is extensively applied in various real-life scenarios. However, recent research has highlighted the severe infringement of privacy and intellectual property caused by model stealing attacks. Therefore, more researchers are dedicated to studying the principles and methods of such attacks to promote the security development of artificial intelligence. Most of the existing model stealing attacks rely on prior information of the attacked models and consider ideal conditions. In order to better understand and defend against model stealing in real-world scenarios, we propose a novel model stealing method, named STMS, based on causal inference learning. For the first time, we introduce the problem of out-of-distribution generalization into the model stealing domain. The proposed approach operates under more challenging conditions, where the training and testing data of the target model are unknown, black-box, hard-label outputs, and there is a distribution shift during the testing phase. STMS achieves comparable or better stealing accuracy and generalization performance than prior works on multiple datasets and tasks. Moreover, this universal framework can be applied to improve the effectiveness of other model stealing methods and can also be migrated to other areas of machine learning.

Non-IID data presents a significant challenge for federated learning(FL), and personalized FL is a natural solution to address this challenge. Recently, Graph Neural Network (GNN) has recently emerged to model the complex client relationship using a client graph to refine personalized models. However, this approach depends on an existing client relation graph on the server, making it impractical unless this prerequisite is satisfied. Furthermore, noisy and missing connections in the original graph structures can degrade personalization performance. In this work, we propose an unsupervised structure learning approach to improve personalized FL, where the server learns a dynamic client graph through self-supervision and generates structure-based client representations. These representations are then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies on benchmark datasets demonstrate the significant effectiveness of our approach and the high quality of the client graphs. The code is available at https://github.com/lazyJane/FedSKA.

The semi-supervised node classification task is a basic problem in graph neural networks(GNNs). GNNs have shown their superiority in graph datasets over traditional neural networks such as Multilayer Perceptron. However, due to the limitation of Weisfeiler-Lehman, the existing GNNs will discard some prior knowledge, which is hard to be coped with, such as Dropout skill, etc. In this paper, we proposed a framework called KAFNN to introduce knowledge discarded obliviously to enhance data representation. KAFNN, based on the Siamese network, introduces the framework of combining GNNs and deep neural networks(DNNs) to capture the data presentation as whole as possible, which will inject more knowledge into GNNs. Extensive experiments based on seven public datasets and seven GNN models have shown that KAFNN has promoted presentation of several state-of-the-art GNN models in a competitive performance.

Node classification is a fundamental research problem in graph neural networks(GNNs), which uses node's feature and label to capture node embedding in a low dimension. The existing graph node classification approaches mainly focus on GNNs from global and local perspectives. The relevant research is relatively insufficient for the micro perspective, which refers to the feature itself. In this paper, we prove that deeper GCNs' features will be updated with the same coefficient in the same dimension, limiting deeper GCNs' expression. To overcome the limits of the deeper GCN model, we propose a zero feature (k-ZF) method to train GCNs. Specifically, k-ZF randomly sets the initial k feature value to zero, acting as a data rectifier and augmenter, and is also a skill equipped with GCNs models and other GCNs skills. Extensive experiments based on three public datasets show that k-ZF significantly improves GCNs in the feature aspect and achieves competitive accuracy.