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学者姓名：黄维
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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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Spatio-temporal prediction is a pivotal service for smart city applications, such as traffic and air quality prediction. Deep learning models are widely employed for this task, but the effectiveness of existing methods heavily depends on large amounts of data from urban sensors. However, in the early stages of smart city development, data scarcity poses a significant challenge due to the limited data collected from newly deployed sensors. Moreover, transferring data from other resource-rich cities is typically infeasible because of strict privacy policies. To address these challenges, we propose a relational fusion-based hypergraph neural network (RFHGN) for few-sample spatio-temporal prediction. RFHGN is trained directly on limited data within a city, exploiting multiple spatial correlations and hierarchical temporal dependencies to enrich spatio-temporal representations. Specifically, to enhance spatial expressiveness, we design a high-order spatial relation-aware learning module with an adaptive time-varying hypergraph structure. This structure is learned by integrating observational data and is iteratively updated during training, enabling the capture of dynamic high-order interactions. By combining these interactions with pairwise spatial representations, we derive mixed-order spatial representations. To reduce potential redundancy, we introduce a regularized independence loss to ensure the independence of pairwise and high-order spatial representations. Additionally, to effectively capture temporal dependencies at micro and macro levels, we develop a hierarchical temporal relation-aware learning module. Extensive experiments on three spatio-temporal prediction tasks: traffic flow, traffic speed, and air quality prediction demonstrate that RFHGN outperforms state-of-the-art baselines.
Keyword ：
Few-sample learning Few-sample learning Graph neural network Graph neural network Hypergraph learning Hypergraph learning Spatio-temporal prediction Spatio-temporal prediction
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| GB/T 7714 | Ouyang, Xiaocao , Li, Yanhua , Guo, Dongyu et al. Enhancing few-sample spatio-temporal prediction via relational fusion-based hypergraph neural network [J]. | INFORMATION FUSION , 2025 , 121 . |
| MLA | Ouyang, Xiaocao et al. "Enhancing few-sample spatio-temporal prediction via relational fusion-based hypergraph neural network" . | INFORMATION FUSION 121 (2025) . |
| APA | Ouyang, Xiaocao , Li, Yanhua , Guo, Dongyu , Huang, Wei , Yang, Xin , Yang, Yan et al. Enhancing few-sample spatio-temporal prediction via relational fusion-based hypergraph neural network . | INFORMATION FUSION , 2025 , 121 . |
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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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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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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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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.
Keyword ：
Federated learning Federated learning Machine learning Machine learning Multimodal fusion Multimodal fusion Multimodal learning Multimodal learning Privacy protection Privacy protection
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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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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.
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, Jia , Yang, Nijing , Lee, Yanli et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection [J]. | INFORMATION FUSION , 2024 , 112 . |
| MLA | Liu, Jia et al. "FedDAF: Federated deep attention fusion for dangerous driving behavior detection" . | INFORMATION FUSION 112 (2024) . |
| APA | Liu, Jia , Yang, Nijing , Lee, Yanli , Huang, Wei , Du, Yajun , Li, Tianrui et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection . | INFORMATION FUSION , 2024 , 112 . |
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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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