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学者姓名：黄维
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Abstract ：
Many representation learning methods have gradually emerged to better exploit the properties of multi-view data. However, these existing methods still have the following areas to be improved: 1) Most of them overlook the ex-ante interpretability of the model, which renders the model more complex and more difficult for people to understand; 2) They underutilize the potential of the bi-topological spaces, which bring additional structural information to the representation learning process. This lack is detrimental when dealing with data that exhibits topological properties or has complex geometrical relationships between different views. Therefore, to address the above challenges, we propose an optimization-oriented multi-view representation learning framework in implicit bi-topological spaces. On one hand, we construct an intrinsically interpretability end-to-end white-box model that directly conducts the representation learning procedure while improving the transparency of the model. On the other hand, the integration of bi-topological spaces information within the network via manifold learning facilitates the comprehensive utilization of information from the data, ultimately enhancing representation learning and yielding superior performance for downstream tasks. Extensive experimental results demonstrate that the proposed method exhibits promising performance and is feasible in the downstream tasks.
Keyword ：
Bi-topological spaces Bi-topological spaces Multi-view learning Multi-view learning Optimization-oriented network Optimization-oriented network Representation learning Representation learning White-box model White-box model
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| GB/T 7714 | Lan, Shiyang , Du, Shide , Fang, Zihan et al. Optimization-oriented multi-view representation learning in implicit bi-topological spaces [J]. | INFORMATION SCIENCES , 2025 , 704 . |
| MLA | Lan, Shiyang et al. "Optimization-oriented multi-view representation learning in implicit bi-topological spaces" . | INFORMATION SCIENCES 704 (2025) . |
| APA | Lan, Shiyang , Du, Shide , Fang, Zihan , Cai, Zhiling , Huang, Wei , Wang, Shiping . Optimization-oriented multi-view representation learning in implicit bi-topological spaces . | INFORMATION SCIENCES , 2025 , 704 . |
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With the growing diversity of data sources, multi-view learning methods have attracted considerable attention. Among these, by modeling the multi-view data as multi-view graphs, multi-view Graph Neural Networks (GNNs) have shown encouraging performance on various multi-view learning tasks. The message passing is the critical mechanism empowering GNNs with superior capacity to process complex graph data. However, most multi-view GNNs are designed on the well-established overall framework, overlooking the intrinsic challenges of the message passing on multi-view scenarios. To clarify this, we first revisit the message passing mechanism from a Laplacian smoothing perspective, revealing the key to designing a multi-view message passing. Following the analysis, in this paper, we propose an enhanced GNN framework termed Confluent Graph Neural Networks (CGNN), with Cross-view Confulent Message Pssing (CCMP) tailored for multi-view learning. Inspired by the optimization of an improved multi-view Laplacian smoothing problem, CCMP contains three sub-modules that enable the interaction between graph structures and consistent representations, which makes it aware of consistency and complementarity information across views. Extensive experiments on four types of data including multi-modality data demonstrate that our proposed model exhibits superior effectiveness and robustness. The code is available at https://github.com/shumanzhuang/CGNN. © 2024 ACM.
Keyword ：
Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Federated learning Federated learning Graph algorithms Graph algorithms Graph neural networks Graph neural networks Laplace transforms Laplace transforms Neural network models Neural network models
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| GB/T 7714 | Zhuang, Shuman , Huang, Sujia , Huang, Wei et al. Enhancing Multi-view Graph Neural Network with Cross-view Confluent Message Passing [C] . 2024 : 10065-10074 . |
| MLA | Zhuang, Shuman et al. "Enhancing Multi-view Graph Neural Network with Cross-view Confluent Message Passing" . (2024) : 10065-10074 . |
| APA | Zhuang, Shuman , Huang, Sujia , Huang, Wei , Chen, Yuhong , Wu, Zhihao , Liu, Ximeng . Enhancing Multi-view Graph Neural Network with Cross-view Confluent Message Passing . (2024) : 10065-10074 . |
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Dangerous driving behavior detection is one of the most important researches in Intelligent Transportation System (ITS), which can effectively reduce the probability and number of traffic accidents. Although some recent approaches combined with deep learning techniques have been proposed for detecting dangerous driving behaviors, the protection of user's privacy is neglected. Therefore, we propose a Federated Deep Attention Fusion model (FedDAF) to address the dual security issues in dangerous driving behavior detection, i.e., data security and traffic security. On the Client side, we design the Deep Attention Fusion Network for extracting and learning driving process features as well as fusing the environmental factors of the vehicle in driving. On the Server side, the Singular Spectrum Entropy Aggregation method is designed to aggregate Clients with high relevance and multiple information content, thereby realizing safety information sharing among Clients. Finally, the experimental results on real datasets show that the FedDAF method has the best performance on several evaluation metrics relative to the existing two categories of benchmark methods. © 2024 Elsevier B.V.
Keyword ：
Dangerous driving behavior detection Dangerous driving behavior detection Data fusion Data fusion Deep learning Deep learning Federated learning Federated learning Intelligent transportation system Intelligent transportation system
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| GB/T 7714 | Liu, J. , Yang, N. , Lee, Y. et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection [J]. | Information Fusion , 2024 , 112 . |
| MLA | Liu, J. et al. "FedDAF: Federated deep attention fusion for dangerous driving behavior detection" . | Information Fusion 112 (2024) . |
| APA | Liu, J. , Yang, N. , Lee, Y. , Huang, W. , Du, Y. , Li, T. et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection . | Information Fusion , 2024 , 112 . |
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Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously. However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR. To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information. Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server. Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module. Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains.
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| GB/T 7714 | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning [J]. | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 , 2024 : 2153-2161 . |
| MLA | Lin, Zhenghong et al. "Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning" . | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 (2024) : 2153-2161 . |
| APA | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu , Xu, Jiayu , Liu, Weiming , Liao, Xinting et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning . | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 , 2024 , 2153-2161 . |
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Multimodal learning mines and analyzes multimodal data in reality to better understand and appreciate the world around people. However, how to exploit this rich multimodal data without violating user privacy is a key issue. Federated learning is a privacy-conscious alternative to centralized machine learning, therefore many researchers have combined federated learning with multimodal learning to break down data barriers for the purpose of jointly leveraging multiple modal data from different clients for modeling. In order to provide a systematic summarize of multimodal federated learning, this paper describes the basic mode of multimodal federated learning, multimodal fusion based on federated learning, multimodal federated learning optimization and multimodal federated learning application, and introduces each type of multimodal federated learning methods in detail. Finally, the future research trends of multimodal federated learning are discussed and analyzed, mainly including the optimization of multimodal federated learning, privacy-preserving techniques for multimodal federated learning, multimodal federated few-shot learning & multimodal federated semi-supervised learning, and data and knowledge-driven multimodal federated learning. © 2024 Elsevier B.V.
Keyword ：
Learning systems Learning systems Modal analysis Modal analysis Privacy-preserving techniques Privacy-preserving techniques
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| GB/T 7714 | Huang, Wei , Wang, Dexian , Ouyang, Xiaocao et al. Multimodal federated learning: Concept, methods, applications and future directions [J]. | Information Fusion , 2024 , 112 . |
| MLA | Huang, Wei et al. "Multimodal federated learning: Concept, methods, applications and future directions" . | Information Fusion 112 (2024) . |
| APA | Huang, Wei , Wang, Dexian , Ouyang, Xiaocao , Wan, Jihong , Liu, Jia , Li, Tianrui . Multimodal federated learning: Concept, methods, applications and future directions . | Information Fusion , 2024 , 112 . |
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Software-Defined Networking （SDN） is a widely adopted network paradigm characterized by the separation of the control plane from the data plane. In light of network security threats， particularly Distributed Denial of Service （DDoS） attacks， the integration of effective DDoS attack detection methods within SDN is of paramount importance. The centralized control characteristic of SDN presents significant security risks when employing centralized DDoS attack detection methods， thereby posing considerable challenges to the security of the control plane in SDN environments. Furthermore， the growing volume of traffic data in SDN environments results in challenges related to more intricate traffic characterization and a pronounced Non-Independent and Identically Distributed （Non-IID） distribution among various entities. These issues present significant barriers to enhancing the accuracy and robustness of current federated learning-based detection models. The separation of management and control in SDN facilitates the creation of new flow rules by users， which enhances the efficiency of message routing control. However， current methodologies for flow detection face difficulties in preserving the knowledge of original features while simultaneously adapting to the distribution of newly generated features within the SDN environment. This challenge contributes to a phenomenon known as data forgetting. Furthermore，the imposition of flow rules restricts the forwarding targets of messages， resulting in variability in the data messages that can be collected by different host entities. The Non-IID distribution problem significantly undermines the performance and robustness of DDoS attack detection models that utilize artificial intelligence. To address these challenges， we propose a federated incremental learning-based model for DDoS attack detection within an SDN environment. This model integrates incremental learning and federated learning to accommodate new data inputs through incremental model updates， thereby eliminating the need for global re-training of the entire model. To mitigate the security risks associated with centralized DDoS attack detection methods and to address the Non-IID distribution issues arising from data increments， we introduce a weighted aggregation algorithm grounded in federated incremental learning. This algorithm personalizes adaptation to different subdataset increments by dynamically adjusting aggregation weights， thereby enhancing the efficiency of incremental aggregation. Additionally， in response to the complex traffic features inherent in SDN networks， we propose a DDoS attack detection methodology that employs Long Short-Term Memory （LSTM） networks. This approach enables real-time detection of traffic features by extracting and learning the temporal correlations present in the data， utilizing statistical analysis of the temporal characteristics of traffic data within SDN networks. Finally， by integrating the unique characteristics of SDN networks， we facilitate real-time decision-making for DDoS defense. This integration combines the results of DDoS attack detection with information pertaining to network entities， enabling the real-time deployment of flow rules. Concurrently， this approach effectively mitigates malicious DDoS attack traffic， safeguards critical entities， and ensures the stability of network topology. In this study， we evaluate the performance of the proposed method against existing techniques， including FedAvg， FA-FedAvg， and FIL-IIoT， in the context of an incremental DDoS attack detection task. The experimental results indicate that the proposed method enhances the accuracy of DDoS attack detection by an improvement range of 5. 06% to 12. 62% and increases the F1-Score by 0. 0565 to 0. 1410 when compared to alternative methods. © 2024 Science Press. All rights reserved.
Keyword ：
cybersecurity cybersecurity DDoS attack detection DDoS attack detection federated incremental learning federated incremental learning federated learning federated learning software-defined networks software-defined networks
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| GB/T 7714 | Liu, Y.-H. , Fang, W.-Y. , Guo, W.-Z. et al. Federated Incremental Learning Based DDoS Attack Detection Model in SDN Environment; [基于联邦增量学习的 SDN 环境下DDoS 攻击检测模型] [J]. | Chinese Journal of Computers , 2024 , 47 (12) : 2852-2866 . |
| MLA | Liu, Y.-H. et al. "Federated Incremental Learning Based DDoS Attack Detection Model in SDN Environment; [基于联邦增量学习的 SDN 环境下DDoS 攻击检测模型]" . | Chinese Journal of Computers 47 . 12 (2024) : 2852-2866 . |
| APA | Liu, Y.-H. , Fang, W.-Y. , Guo, W.-Z. , Zhao, B.-K. , Huang, W. . Federated Incremental Learning Based DDoS Attack Detection Model in SDN Environment; [基于联邦增量学习的 SDN 环境下DDoS 攻击检测模型] . | Chinese Journal of Computers , 2024 , 47 (12) , 2852-2866 . |
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Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.
Keyword ：
Anomaly detection Anomaly detection Federated learning Federated learning Internet of things Internet of things Intrusion detection Intrusion detection
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| GB/T 7714 | Zhang, Hao , Ye, Junwei , Huang, Wei et al. Survey of federated learning in intrusion detection [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2024 , 195 . |
| MLA | Zhang, Hao et al. "Survey of federated learning in intrusion detection" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 195 (2024) . |
| APA | Zhang, Hao , Ye, Junwei , Huang, Wei , Liu, Ximeng , Gu, Jason . Survey of federated learning in intrusion detection . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2024 , 195 . |
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SDN是一种被广泛应用的网络范式.面对DDoS攻击等网络安全威胁,在SDN中集成高效的DDoS攻击检测方法尤为重要.由于SDN集中控制的特性,集中式DDoS攻击检测方法在SDN环境中存在较高的安全风险,使得SDN的控制平面安全性受到了巨大挑战.此外,SDN环境中流量数据不断增加,导致复杂流量特征的更复杂化、不同实体之间严重的Non-IID分布等问题.这些问题对现有的基于联邦学习的检测模型准确性与鲁棒性的进一步提高造成严重阻碍.针对上述问题,本文提出了一种基于联邦增量学习的SDN环境下DDoS攻击检测模型.首先,为解决集中式DDoS攻击检测的安全风险与数据增量带来的Non-IID分布问题,本文提出了一种基于联邦增量学习的加权聚合算法,使用动态调整聚合权重的方式个性化适应不同子数据集增量情况,提高增量聚合效率.其次,针对SDN环境中复杂的流量特征,本文设计了一种基于LSTM的DDoS攻击检测方法,通过统计SDN环境中流量数据的时序特征,提取并学习数据的时序关特征的相关性,实现对流量特征数据的实时检测.最后,本文结合SDN集中管控特点,实现了SDN环境下的DDoS实时防御决策,根据DDoS攻击检测结果与网络实体信息,实现流规则实时下发,达到有效阻断DDoS攻击流量、保护拓扑重要实体并维护拓扑流量稳定的效果.本文将提出的模型在增量式DDoS攻击检测任务上与FedAvg、FA-FedAvg和FIL-IIoT三种方法进行性能对比实验.实验结果表明,本文提出方法相比于其他方法,在DDoS攻击检测准确率上提升5.06%～12.62%,F1-Score提升0.0565～0.1410.
Keyword ：
DDoS攻击检测 DDoS攻击检测 网络安全 网络安全 联邦增量学习 联邦增量学习 联邦学习 联邦学习 软件定义网络 软件定义网络
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| GB/T 7714 | 刘延华 , 方文昱 , 郭文忠 et al. 基于联邦增量学习的SDN环境下DDoS攻击检测模型 [J]. | 计算机学报 , 2024 , 47 (12) : 2852-2866 . |
| MLA | 刘延华 et al. "基于联邦增量学习的SDN环境下DDoS攻击检测模型" . | 计算机学报 47 . 12 (2024) : 2852-2866 . |
| APA | 刘延华 , 方文昱 , 郭文忠 , 赵宝康 , 黄维 . 基于联邦增量学习的SDN环境下DDoS攻击检测模型 . | 计算机学报 , 2024 , 47 (12) , 2852-2866 . |
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As deep learning has continuously made breakthroughs in computer vision, Image Forgery Localization (IFL) task has also started using deep learning frameworks. Currently, most deep learning-based IFL methods use binary cross entropy as the loss function during model training. However, the number of tampered pixels in image forgery is significantly smaller than the number of real pixels. This disparity makes it easier for the model to classify samples as real pixels during training, leading to a reduced F1 score. Therefore, in this paper, we have proposed a loss function for the IFL task: Forgery Loss. The Forgery Loss assigns weight to the classification loss of tampered pixels and edges, enhances tampered pixel constraints in the model, and amplifies the importance of difficult-to-classify samples. These enhancements facilitate the model to acquire more productive information. Consequently, the F1 score of the model is enhanced. Additionally, we designed an end-to-end, pixel-level detection network DSSE-Net. It comprises of a dual-stream codec network that extracts high-level and low-level features of images, and an edge attention stream. The edge attention stream have a Edge Attention Model which enhances the network's attention to the high frequency edges of the image and, in conjunction with the edge enhancement algorithm in Forgery Loss, improves the model's ability to detect tampered edges. Experiments demonstrate that Forgery Loss can effectively improve the F1 score, while the DSSE-Net accuracy outperforms the current SOTA algorithm.
Keyword ：
Attention mechanisms Attention mechanisms Dual stream network Dual stream network Forgery loss Forgery loss Image forgery localization Image forgery localization
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| GB/T 7714 | Zheng, Aokun , Huang, Tianqiang , Huang, Wei et al. DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (6) : 2323-2335 . |
| MLA | Zheng, Aokun et al. "DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 6 (2023) : 2323-2335 . |
| APA | Zheng, Aokun , Huang, Tianqiang , Huang, Wei , Huang, Liqing , Ye, Feng , Luo, Haifeng . DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (6) , 2323-2335 . |
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合成了含对苯氧基联接链的羧酸盐Gemini表面活性剂,研究了其胶团化特性.结果表明,该羧酸盐Gemini表面活性剂具有很低的cmc值,给出了cmc-T(温度)以及lncmc-(m+1)(烷烃链长)的回归方程.计算了胶团化的热力学函数变化,证实胶团化过程来自熵驱动,并表现出焓/熵补偿现象,在所考察的系列中,以(m+1)=11的胶团最为稳定.
Keyword ：
合成 合成 对苯氧基联接链 对苯氧基联接链 羧酸盐Gemini表面活性剂 羧酸盐Gemini表面活性剂 胶团化 胶团化
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| GB/T 7714 | 杜恣毅 , 游毅 , 姜蓉 et al. 含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性 [J]. | 高等学校化学学报 , 2003 , 24 (11) : 2056-2059 . |
| MLA | 杜恣毅 et al. "含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性" . | 高等学校化学学报 24 . 11 (2003) : 2056-2059 . |
| APA | 杜恣毅 , 游毅 , 姜蓉 , 黄维 , 郑欧 , 黄长沧 et al. 含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性 . | 高等学校化学学报 , 2003 , 24 (11) , 2056-2059 . |
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