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Guangyu Li

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction

Hang Zhou
Wentao Yu
Yang Wei
Guangyu Li
Sha Xu
Chen Gong

Traffic prediction plays an important role in urban management. However, existing methods rely on centralized traffic data, which may raise privacy concerns. Federated traffic prediction offers a promising solution for clients (e.g., traffic management administrations) in different regions to collaboratively train models in a distributed manner without exposing private data. Nonetheless, data isolation inherently breaks the correlations between nodes (i.e., traffic sensors collecting data) from different regions, which leads to the missing inter-client dependency. Consequently, current works either fail to capture the missing inter-client dependency or compromise data privacy to recover the inter-client dependency. To address this issue, we propose a novel Federated method which recovers the inter-client dependency with HIdden global componeNTs (FedHINT). We find that the traffic data from different local regions actually contain hidden global components that reflect cross-regional traffic changes. Therefore, our FedHINT aims to extract hidden global components from each client to generate proxy nodes that represent global information, which are then utilized to recover the inter-client dependency. To be specific, we employ an attention module, which is guided by the shared global queries to capture hidden global components from local traffic data, to generate proxy nodes. Subsequently, our FedHINT adaptively learns the correlations between proxy nodes and local nodes through a global encoder. During this process, the global information in proxy nodes compensate for the loss of information from cross-regional nodes, which thereby recovers the missing inter-client dependency. Intensive experiments on multiple datasets demonstrate that our FedHINT significantly outperforms the state-of-the-art methods, with an average decrease of 3.73 and 4.81 on MAE and RMSE, respectively.

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AAAI Conference 2025 Conference Paper

Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection

Zhipeng Zou
Sheng Wan
Guangyu Li
Bo Han
Tongliang Liu
Lin Zhao
Chen Gong

Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the centroids of in-distribution (ID) examples, have demonstrated promising OOD detection performance. However, the objectives utilized in prior approaches are typically designed for classification and thus might not yield sufficient discriminative power to distinguish between ID and OOD examples. Therefore, this paper proposes a prototype-based contrastive learning framework for OOD detection, which is termed provable Discriminative Hyperspherical Embedding (DHE). The proposed framework provides a theoretical analysis of inter-class dispersion, which is proved to be fundamental in reducing the false positive rate (FPR) on OOD examples. Based on this, we devise an angular spread loss to achieve the maximal dispersion of the prototypes of different classes prior to training. Subsequently, a prototype-enhanced contrastive loss is introduced to align embeddings of ID examples closely with their corresponding prototypes. In our proposed DHE, the maximal prototype dispersion is theoretically proved, thereby avoiding the pitfalls of local optima commonly encountered by most existing methods. Experimental results demonstrate the effectiveness of our proposed DHE, which showcases a remarkable reduction in FPR95 (i.e., 5.37% on CIFAR-100) and more than doubling the computational efficiency when compared with the state-of-the-art methods.

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ECAI Conference 2025 Conference Paper

TD-RGFL: A Two-Stage Dual-Branch Robust Framework for Addressing Heterogeneous Label Noise in Graph Federated Learning

Rongze Xu
Hai Cao
Xuanpeng Li
Chen Gong 0002
Guangyu Li

Graph Federated Learning (GFL) leverages distributed collaborative training for global graph neural networks and it is widely applied in many fields, such as social network analysis, recommendation systems and so on. However, GFL often suffers from heterogeneous label noise interferences across clients, and existing studies mainly handle label noise issues for images, where the data is independent and the labeled samples are sufficient. However, these methods are not suitable for GFL scenarios and they fail to cope with the challenges of label noise from graph-structured data, in which nodes are dependent and label information is highly sparse. To address the above problems, we propose a Two-stage Dual-branch Robust Graph Federated Learning Framework (TD-RGFL). Specifically, based on the established neighborhood conflict estimation scheme, we first design an available noisy clients identification algorithm, to effectively utilize the limited label information in graph data and capture the characteristics of noisy information. In addition, we propose a feasible dual-branch collaborative training method by means of debiased knowledge distillation, so as to fully exploit the useful data information of noisy clients and enhance the overall robustness of federated learning. Moreover, we devise a flexible federated aggregation and local model update strategy based on the proximal regularization principle, to further reduce the negative impact of noisy clients and improve the generalization ability of the global model across different clients. Finally, we implement a series of experiments, and the corresponding results indicate that our algorithm significantly outperforms existing methods under different conditions.

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ECAI Conference 2024 Conference Paper

MFF-YOLO: Multi-scale Feature Fusion Network for Small Ship Detection in Night Scenes

Guangyu Li
Jun Li 0027
Hai Cao
Houjun Wang
Weili Guo
Chen Gong 0002

Ship detection plays a critical role in intelligent maritime applications including port management, marine monitoring and so on. Most existing ship detectors are trained on high-quality conventional-sized ship images under normal lighting conditions. However, low-quality images with poor lighting conditions often exist, where the features are difficult to be distinguished. Additionally, small ships with fewer pixels exhibit minimal appearance information and weak contour characteristics in night scenes, which are harmful to the multi-scale feature fusion. To address the above challenges, we propose an effective multi-scale feature fusion network for small ship detection in night scenes named MFF-YOLO. Specifically, we first design a night-friendly enhanced channel attention module, to better represent channel-dimensional features of small ships. In addition, we construct a multi-scale feature fusion architecture based on space and channel, to obtain richer semantic information of small ships in poor lighting conditions and further enhance the feature distinguishability. Finally, a series of experiments are implemented and corresponding results demonstrate the effectiveness and feasibility of our proposed method.

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AAAI Conference 2020 Conference Paper

Generative Attention Networks for Multi-Agent Behavioral Modeling

Guangyu Li
Bo Jiang
Hao Zhu
Zhengping Che
Yan Liu

Understanding and modeling behavior of multi-agent systems is a central step for artiﬁcial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identiﬁes agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identiﬁcation. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

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