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Data-Agnostic Model Poisoning Against Federated Learning: A graph autoencoder Approach

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 3465-3480页

作者： Li, Kai Zheng, Jingjing Yuan, Xin Ni, Wei Akan, Ozgur B. Poor, H. Vincent Univ Cambridge Dept Engn Cambridge CB3 0FA England Real Time & Embedded Comp Syst Res Ctr CISTER P-4249015 Porto Portugal Carnegie Mellon Univ CyLab Secur & Privacy Inst Pittsburgh PA 15213 USA Real Time & Embedded Comp Syst Res Ctr CISTER P-4249015 Porto Portugal Commonwealth Sci & Ind Res Org CSIRO Digital Prod & Serv Flagship Sydney NSW 2122 Australia Univ Cambridge Dept Engn Div Elect Engn Cambridge CB3 0FA England Koc Univ Ctr neXt generat Commun CXC TR-34450 Istanbul Turkiye Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL.

关键词： Federated learning model poisoning attack graph autoencoder feature correlation

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Full graph autoencoder for One-Class Group Anomaly Detection of IIoT System

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IEEE INTERNET OF THINGS JOURNAL 2022年第21期9卷 21886-21898页

作者： Feng, Yong Chen, Jinglong Liu, Zijun Lv, Haixin Wang, Jun Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Xian 710049 Peoples R China Mech & Environm Res Ctr Rocket Engine Sci & Technol Liquid Rocket Engine Lab Xian 710100 Peoples R China

With the increasing automation and integration of equipment, it is urgent to carry out anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial Internet of Things (IIoT). Recently developed intelligent methods focus on component-level diagnosis or detection, resulting in difficulty in the health assessment of system with multisource data coupling. In addition, data-driven methods rarely emphasize the use of knowledge from the real physical system. In this article, we propose a full graph autoencoder to perform one-class group AD for the large-scale IIoT system. The proposed model takes as input data of normal status at training and only comprises several normalized graph convolutional layers, thus it is simple and fast. Different from Euclidean-based methods, the proposed model can handle various irregular structures together. For graph learning, multivariate time series are converted into graph data fused with prior knowledge. To achieve AD, we propose to reconstruct the full graph for the first time to obtain a reliable anomaly score. Besides, we extend a variational model to fully learn the graph representation. Moreover, a graph augmentation operation is employed to improve the accuracy and robustness. The proposed models are evaluated on two multisensor data sets from liquid rocket engine (LRE) systems, and the experimental results demonstrate the effectiveness and generalization of the IIoT system.

关键词： Industrial Internet of Things Anomaly detection Rockets Liquids Engines Data models Time series analysis graph autoencoder group anomaly detection (GAD) Industrial Internet of Things (IIoT) multivariate time series (MTS)

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A New graph autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 2292-2303页

作者： Wang, Juan Qiao, Tian-Jing Zheng, Chun-Hou Liu, Jin-Xing Shang, Jun-Liang Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering. In this paper, we propose scGAMF, a graph autoencoder-based multi-level kernel subspace fusion framework for scRNA-seq data analysis. Based on multiple top feature sets, scGAMF unifies deep feature embedding and kernel space analysis into a single framework to learn an accurate clustering affinity matrix. First, we construct multiple top feature sets to avoid the high variability caused by single feature set learning. Second, scGAMF uses a graph autoencoder (GAEs) to extract deep information embedded in the data, and learn embeddings including gene expression patterns and cell-cell relationships. Third, to fully explore the deep potential relationships between cells, we design a multi-level kernel space fusion strategy. This strategy uses a kernel expression model with adaptive similarity preservation to learn a self-expression matrix shared by all embedding spaces of a given feature set, and a consensus affinity matrix across multiple top feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Extensive validation on real datasets shows that scGAMF achieves higher clustering accuracy than many popular single-cell analysis methods.

关键词： Kernel Feature extraction Data models Clustering methods Adaptation models Accuracy Sequential analysis Consensus learning graph autoencoder kernel self-expression model multi-level kernel space fusion multiple top fea-ture sets single-cell type identification

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Robustness meets accuracy in adversarial training for graph autoencoder

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NEURAL NETWORKS 2023年 157卷 114-124页

作者： Zhou, Xianchen Hu, Kun Wang, Hongxia Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410072 Hunan Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410072 Hunan Peoples R China

graph autoencoder (GAE) is an effective deep method for graph embedding, while it is vulnerable to the graph adversarial attacks. Adversarial training, which generates adversarial examples in the adversarial scope(neighborhood of natural examples), is effective to improve the robustness of GAE. However, it may lead to degradation of natural accuracy (accuracy on natural examples) due to the extra training examples generated in the adversarial scope (the reasonable scope of adversarial examples). Therefore, considering robustness and natural accuracy is crucial to GAE. In this paper, an improved GAE model is formulated by combining the Structure and Feature encoders, and a novel Adversarial Training strategy (GAE-SFAT) is proposed based on improved GAE. GAE-SFAT has a smaller but more reasonable adversarial scope for adversarial training, which keeps the robustness and reduces the degradation of natural accuracy compared with ordinary adversarial training. In addition, a novel algorithm considering the robustness and accuracy is designed to optimize the GAE-SFAT. We conduct experiments both on the natural graphs as well as perturbed graphs for three datasets. The results show that GAE-SFAT can perform better than state of arts adversarial training model under different kinds of perturbations. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： graph embedding Adversarial training graph autoencoder Robustness

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Self-supervised graph autoencoder with redundancy reduction for community detection

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NEUROCOMPUTING 2024年 590卷

作者： Wang, Xiaofeng Shen, Guodong Zhang, Zengjie Lai, Shuaiming Zhu, Shuailei Chen, Yuntao Quan, Daying China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China

Community detection is a significant research topic in network science, which has been revisited with graph neural networks. As a powerful graph representation learning model, graph autoencoder (GAE) is commonly used for unsupervised community detection. However, most GAE-based methods ignore multi -scale features of encoding layers, which inherently provide useful information for community detection. Moreover, these methods fail to simultaneously improve the representation learning process and clustering performance through a unified objective function. To address these issues, we propose a self -supervised graph autoencoder model with redundancy reduction for community detection. Firstly, we introduce a multi -scale module based on GAE to obtain discriminative node representations from different encoding layers. In particular, a redundancy reduction strategy is employed to eliminate redundancy information in the latent embedding space. Then, a node clustering module is used to obtain initial community labels. To fully utilize the multi -scale features to further refine clustering performance, a self -supervised module is designed to utilize current clustering labels to supervise the node representation learning process, thus constructing an end -to -end model for community detection. Finally, we validate the effectiveness of the proposed method on real -world networks. Experimental results demonstrate that our method outperforms several state-of-the-art methods in community detection.

关键词： graph autoencoder Community detection Multi-scale features Self-supervised learning Redundancy reduction

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Multi-view representation model based on graph autoencoder

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INFORMATION SCIENCES 2023年第1期632卷 439-453页

作者： Li, Jingci Lu, Guangquan Wu, Zhengtian Ling, Fuqing Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Suzhou Univ Sci & Technol Elect & Informat Engn Suzhou 215009 Peoples R China

graph representation learning is a hot topic in non-Euclidean data in various domains, such as social networks, biological networks, etc. When some data labels are missing, graph autoencoder and graph variational autoencoder can perform outstanding abilities on node clustering or link prediction tasks. However, most existing graph representation learning ignores data's multi-modal features and takes the node features and graph structure features as one view. Besides, most graph autoencoders only reconstruct the node feature matrix or adjacency matrix, which does not fully use the hidden representation information. In this paper, we propose a multi-view representation model based on graph autoencoder, which can employ the global structure topology, latent local topology, and feature relative information. Meanwhile, we add another decoder to reconstruct the node feature matrix as an auxiliary task. In this way, the proposed framework can utilize the learned representation sufficiently. We validate the effectiveness of our framework on four datasets, and the experimental results demonstrate superior performance compared with other advanced frameworks.

关键词： graph representation learning graph autoencoder Unsupervised learning Multi-view learning

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SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction

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BRIEFINGS IN BIOINFORMATICS 2022年第5期23卷 bbac340-bbac340页

作者： Ma, Mingyuan Na, Sen Zhang, Xiaolu Chen, Congzhou Xu, Jin Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA City Univ Hong Kong Dept Informat Syst Hong Kong Peoples R China Peking Univ Beijing Peoples R China

Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.

关键词： miRNA-disease associations prediction graph autoencoder self-feature embedding attention mechanism

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stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第1期25卷 bbad500页

作者： Fang, Zhaoyu Liu, Teng Zheng, Ruiqing Jin, A. Yin, Mingzhu Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Chongqing Univ Gorges Hosp 3 Med Pathol Ctr MPC Clin Res Ctr CRCCanc Early Detect & Treatment Ctr Chongqing 404031 Peoples R China

With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.

关键词： spatial transcriptomics spatial domain graph neural network adversarial learning graph autoencoder

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SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder

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METHODS 2024年 221卷 73-81页

作者： Xu, Lei Fu, Xiangzheng Zhuo, Linlin Zhou, Zhecheng Liao, Xuefeng Tian, Sha Kang, Ruofei Chen, Yifan Macau Univ Sci & Technol Macau Inst Appl Res Med & Hlth Nehers Biophys Lab Innovat Drug Discovery State Key Lab Qual Res Chinese Med Macau Peoples R China Wenzhou Univ Technol Wenzhou Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha Hunan Peoples R China Hunan Univ Chinese Med Coll Integrated Chinese & Western Med Dept Internal Med Changsha Hunan Peoples R China Xuhui Excellent Hlth Informat Technol Co Ltd Shanghai Peoples R China

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github .com /22n9n23 /SGAE-MDA.

关键词： Herbal medicines MiRNA-disease associations (MDAs) graph autoencoder Semi-supervised approach Network noise

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Bearing Fault Diagnosis via graph autoencoder Networks with Multi-kernel Subdomain Adversarial Domain Adaptation

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2024年第6期24卷 2831-2846页

作者： Guo, Junfeng Hu, Zeming Wang, Zhiming Zhang, Yushan Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Peoples R China

Unsupervised domain adaptation (UDA) based on transfer learning methods have been widely used in the research of bearing fault diagnosis under variable operating conditions, and some useful results have been achieved. However, conventional UDA methods predominantly prioritize the extraction of class labels and domain labels from the data, neglecting the effect of data architecture information on extracted characteristics. In addition, global domain adaptation methods ignore the relationship between subdomains. Therefore, in this paper, we propose multi-kernel subdomain adversarial domain adaptation for graph autoencoder networks (MSADAGAE) to solve the above problems, which has two key parts. Firstly, multiple graph convolutional blocks are used to obtain graph node features as well as topologies at different scales via residual-connected graph autoencoders (GAEs). Second, a subadaptation module based on multi-layer multi-kernel local maximum mean discrepancy (MLMK-LMMD) is proposed, including a globally aligned domain classifier and subdomain-aligned domain adaptation. Then, the optimization of feature classification boundaries is further enhanced through margin loss regularization. Finally, validation is performed on the public datasets CWRU, JNU, and the results show that the model exhibits good performance even under unbalanced datasets.

关键词： graph autoencoder Margin loss MLMK-LMMD Subdomain adaptation Fault diagnosis

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