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学者姓名：陈哲毅
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Abstract ：
针对车辆移动过程中服务质量(QoS)下降的问题,提出了一种基于凸优化使能深度强化学习的服务迁移(service migration via convex-optimization-enabled deep reinforcement learning,SeMiR)方法.将优化问题分解为两个子问题并分别求解;针对服务迁移子问题,设计了一种基于改进深度强化学习的服务迁移方法,以探索最优迁移策略;针对资源分配子问题,设计了 一种基于凸优化的资源分配方法,以推导给定迁移决策下每台MEC服务器的最优资源分配,提升服务迁移的性能.实验结果表明:与基准方法相比,SeMiR方法能够有效提升车辆的QoS,在各种场景下均展现出更加优越的性能.
Keyword ：
凸优化 凸优化 服务迁移 服务迁移 深度强化学习 深度强化学习 移动边缘计算 移动边缘计算 资源分配 资源分配 车联网 车联网
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| GB/T 7714 | 黄思进 , 文佳 , 陈哲毅 . 面向边缘车联网系统的智能服务迁移方法 [J]. | 系统仿真学报 , 2025 , 37 (2) : 379-391 . |
| MLA | 黄思进 等. "面向边缘车联网系统的智能服务迁移方法" . | 系统仿真学报 37 . 2 (2025) : 379-391 . |
| APA | 黄思进 , 文佳 , 陈哲毅 . 面向边缘车联网系统的智能服务迁移方法 . | 系统仿真学报 , 2025 , 37 (2) , 379-391 . |
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在大规模物联网(internet-of-things,IoT)系统中,无人机使能的移动边缘计算(mobile edge computing,MEC)可缓解终端IoT设备的性能限制.然而,由于不均匀的IoT设备分布与低效的问题求解效率,如何在大规模IoT系统中高效执行计算卸载面临着巨大的挑战.现有解决方案通常无法适应动态多变的多无人机场景,导致了低效的资源利用与过度的响应延迟.为解决这些重要挑战,提出了一种新型的面向大规模IoT系统的多无人机部署与协作卸载(multi-UAV deployment and collaborative offloading,MUCO)方法.设计了一种基于约束K-Means聚类的无人机部署方案,在提升服务覆盖率的同时保证覆盖均衡.设计了一种基于多智能体强化学习(multi-agent reinforcement learning,MARL)的多无人机协作卸载策略,将来自IoT设备的卸载请求进行拆分与分布式执行,进而实现高效的协作卸载.大量仿真实验验证了 MUCO方法的有效性.与基准方法相比,MUCO方法在不同场景中平均可以取得约23.82％和28.13％的无人机部署性能提升,且能取得更低的时延和能耗.
Keyword ：
K-Means聚类 K-Means聚类 多智能体强化学习 多智能体强化学习 无人机部署 无人机部署 物联网 物联网 移动边缘计算 移动边缘计算 计算卸载 计算卸载
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| GB/T 7714 | 黄智钦 , 卢恬英 , 陈哲毅 . 面向大规模IoT系统的多无人机部署与协作卸载 [J]. | 系统仿真学报 , 2025 , 37 (1) : 25-39 . |
| MLA | 黄智钦 等. "面向大规模IoT系统的多无人机部署与协作卸载" . | 系统仿真学报 37 . 1 (2025) : 25-39 . |
| APA | 黄智钦 , 卢恬英 , 陈哲毅 . 面向大规模IoT系统的多无人机部署与协作卸载 . | 系统仿真学报 , 2025 , 37 (1) , 25-39 . |
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Shared information refers to the common semantic information across multiple modalities, and complementary information refers to modality-specific information that complements other modalities. How to fully utilize this information is a key issue in the multimodal sentiment analysis. In this paper, we first propose a Slice Aggregation (SA) algorithm to address the issue of correlation over time. We use sliding windows to calculate the horizontal and vertical correlations, and then aggregate slices into a series of chunks, each represents a set of successive slices with consistent correlation. Second, we introduce a Dynamic Fusion (DF) strategy comprising two components: shared information fusion and complementary information fusion. The former utilizes a multilayer perceptron (MLP) to extract high-level shared representations, whereas the latter employs a cross-modal multi-head attention mechanism to fuse low-level complementary information. Finally, we propose an SA-DF framework where SA organizes raw slices into correlation-consistent chunks, and DF progressively fuses features across these chunks. The concatenated fused features are used for final sentiment prediction. The experiments on CMU-MOSI and CH-SIMS datasets show that the proposed SA-DF can achieve the best performance on sentiment analysis tasks when compared with the state-of-the-art baselines. © China Computer Federation (CCF) 2025.
Keyword ：
Cross-modal multi-head attention Cross-modal multi-head attention Dynamic fusion Dynamic fusion Modal correlation Modal correlation Multimodal sentiment analysis Multimodal sentiment analysis Slice aggregation Slice aggregation
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| GB/T 7714 | Zhan, Z. , Cao, D. , Chen, Z. et al. Multimodal sentiment analysis based on slice aggregation and dynamic fusion [J]. | CCF Transactions on Pervasive Computing and Interaction , 2025 . |
| MLA | Zhan, Z. et al. "Multimodal sentiment analysis based on slice aggregation and dynamic fusion" . | CCF Transactions on Pervasive Computing and Interaction (2025) . |
| APA | Zhan, Z. , Cao, D. , Chen, Z. , Cheng, H. , Yu, Z. . Multimodal sentiment analysis based on slice aggregation and dynamic fusion . | CCF Transactions on Pervasive Computing and Interaction , 2025 . |
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Edge anomaly detection guarantees the security of Internet-of-Things (IoT). The emerging Federated Learning (FL) can ameliorate the privacy-leakage and data-island issues in edge anomaly detection. However, existing FL-based solutions still reveal limitations in handling statistical and system heterogeneity, thus they cannot adapt to anomaly detection in complex edge environments. To address these problems, we propose FedGPA, a novel Federated learning with Global-Personalized collaboration for edge Anomaly detection. First, we design a conditional calculation component to transform traffic features into global and personalized feature vectors. Next, we introduce contrast and magnitude losses in the global-class embedding module and guide the learning of global feature vectors with the embedding of sample classes. Then, we adopt cross-entropy loss to guide the learning of personalized feature vectors. Finally, the cosine similarity between the updated gradients of cross-entropy and overall losses is used to determine the loss replacement, thereby accelerating the model training. Notably, we prove the FedGPA can converge stably during the aggregation process. Using real-world testbed and traffic datasets, extensive experiments verify the effectiveness of the FedGPA, which efficiently solves statistical and system heterogeneity. Compared to state-of-the-art methods, the FedGPA achieves higher detection accuracy and shorter training time, exhibiting better scalability and convergence.
Keyword ：
anomaly detection anomaly detection Edge computing Edge computing Federated Learning Federated Learning global-personalized collaboration global-personalized collaboration
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| GB/T 7714 | Chen, Zheyi , Xue, Longxiang , Zhong, Luying et al. FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection [J]. | IEEE INFOCOM 2025-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2025 . |
| MLA | Chen, Zheyi et al. "FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection" . | IEEE INFOCOM 2025-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (2025) . |
| APA | Chen, Zheyi , Xue, Longxiang , Zhong, Luying , Min, Geyong . FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection . | IEEE INFOCOM 2025-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2025 . |
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Anomaly traffic detection offers essential technical support for securing Mobile Edge Computing (MEC) networks. The emerging Large Model (LM) has attracted much attention for their excellent data generation and processing capabilities, but it is difficult to deploy LM-based detection models in resource-constrained MEC networks. Existing solutions usually compress large models into tiny ones, but they tend to be impacted by data drift, resulting in decreased detection accuracy. To address this key challenge, we propose CL4Det, a novel multi-model anomaly traffic detection framework with LM-powered continuous learning, where the tiny models deployed in MEC networks can achieve the desired performance comparable to the large models via continuous retraining. Specifically, CL4Det periodically evaluates the model performance degradation caused by data drift in MEC networks and decides whether to generate retraining tasks and their configurations. Meanwhile, CL4Det schedules all traffic detection and retraining tasks with proper resource allocation, aiming to ensure real-time detection and maximize model accuracy. A case study with real-world traffic datasets verifies the effectiveness and superiority of CL4Det. Finally, we outline the challenges and future directions to fully exploit the collaborative potentials of MEC networks and LM in anomaly traffic detection.
Keyword ：
Accuracy Accuracy Anomaly detection Anomaly detection Computational modeling Computational modeling Data collection Data collection Data models Data models Edge computing Edge computing Graphics processing units Graphics processing units Image edge detection Image edge detection Mobile computing Mobile computing Multi-access edge computing Multi-access edge computing Real-time systems Real-time systems Schedules Schedules Servers Servers Solid modeling Solid modeling Telecommunication traffic Telecommunication traffic Training Training
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| GB/T 7714 | Zhang, Junjie , Chen, Zheyi , Cheng, Hongju et al. Improving Multi-Model Anomaly Traffic Detection in MEC Networks With Large-Model- Powered Continuous Learning [J]. | IEEE NETWORK , 2025 , 39 (3) : 56-62 . |
| MLA | Zhang, Junjie et al. "Improving Multi-Model Anomaly Traffic Detection in MEC Networks With Large-Model- Powered Continuous Learning" . | IEEE NETWORK 39 . 3 (2025) : 56-62 . |
| APA | Zhang, Junjie , Chen, Zheyi , Cheng, Hongju , Li, Jie , Min, Geyong . Improving Multi-Model Anomaly Traffic Detection in MEC Networks With Large-Model- Powered Continuous Learning . | IEEE NETWORK , 2025 , 39 (3) , 56-62 . |
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Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios.
Keyword ：
convex optimization convex optimization deep reinforcement learning deep reinforcement learning Deep reinforcement learning Deep reinforcement learning Delays Delays Electronic mail Electronic mail Internet-of-Vehicles (IoV) Internet-of-Vehicles (IoV) Mobile computing Mobile computing Mobile edge computing (MEC) Mobile edge computing (MEC) Optimization Optimization Quality of service Quality of service Resource management Resource management Servers Servers service migration service migration Training Training Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Chen, Zheyi , Huang, Sijin , Min, Geyong et al. Mobility-Aware Seamless Service Migration and Resource Allocation in Multi-Edge IoV Systems [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (7) : 6315-6332 . |
| MLA | Chen, Zheyi et al. "Mobility-Aware Seamless Service Migration and Resource Allocation in Multi-Edge IoV Systems" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 24 . 7 (2025) : 6315-6332 . |
| APA | Chen, Zheyi , Huang, Sijin , Min, Geyong , Ning, Zhaolong , Li, Jie , Zhang, Yan . Mobility-Aware Seamless Service Migration and Resource Allocation in Multi-Edge IoV Systems . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (7) , 6315-6332 . |
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In Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) can improve the system performance and communication range of intelligent transportation systems (ITSs). However, the resource allocation and computation offloading in UAVs-assisted IoV systems still face huge challenges due to the growing number of vehicle terminals (VTs), potential privacy leakage, and inefficient problem-solving. Existing solutions cannot adapt to such dynamic multi-UAV scenarios and meet the real-time requirements of VTs. To address these challenges, we propose RACOMU, a novel resource allocation and collaborative offloading framework for multi-UAV-assisted IoV. First, we introduce the convex optimization theory to decouple the original problem and then obtain the near-optimal allocation of transmission power and computing resources by solving the Karush-Kuhn-Tucker (KKT) condition. Next, we design a new collaborative offloading strategy with federated deep reinforcement learning (FDRL), where the offloading requests from VTs are processed in a distributed manner to approach the global optimum while preserving data privacy. Extensive experiments verify the effectiveness of the proposed RACOMU. Compared to benchmark methods, RACOMU achieves better performance in terms of task processing latency, decision-making time, and load balancing degree under various scenarios.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Collaboration Collaboration Computational modeling Computational modeling Computation offloading Computation offloading convex optimization convex optimization Delays Delays Energy consumption Energy consumption federated deep reinforcement learning (FDRL) federated deep reinforcement learning (FDRL) Internet of Vehicles (IoV) Internet of Vehicles (IoV) Real-time systems Real-time systems resource allocation resource allocation Resource management Resource management Servers Servers System performance System performance Training Training
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| GB/T 7714 | Chen, Zheyi , Huang, Zhiqin , Zhang, Junjie et al. Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV With Federated Deep Reinforcement Learning [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) : 4629-4640 . |
| MLA | Chen, Zheyi et al. "Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV With Federated Deep Reinforcement Learning" . | IEEE INTERNET OF THINGS JOURNAL 12 . 5 (2025) : 4629-4640 . |
| APA | Chen, Zheyi , Huang, Zhiqin , Zhang, Junjie , Cheng, Hongju , Li, Jie . Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV With Federated Deep Reinforcement Learning . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) , 4629-4640 . |
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The emerging load prediction techniques support up-front and rational resource provisioning in edge systems to enhance system efficiency and Quality-of-Service (QoS). Classic prediction methods may handle loads with apparent trends, but they cannot achieve accurate prediction for highly-variable edge loads. With the advantage of sequential data analysis, recurrent neural networks (RNNs) are often used for load prediction but reveal limited generalization ability and low training efficiency. Moreover, it is hard to obtain a well-performed prediction model by discrete single-edge training with insufficient historical data. To address these important challenges, we propose a novel Multi-edge Cooperative universal framework for load Prediction with Personalized Federated deep learning (MC-2PF), enabling multi-edge cooperative training of load prediction models. Specifically, to solve the client-drift issue in federated learning (FL) caused by distinct data distribution, we customize personalized models for each edge by independent control parameters and theoretically analyze the model convergence improvement. Meanwhile, we prove the generalization bound of the MC-2PF and its universality to RNN-based prediction models through a practical example. Using the real-world testbed and load datasets, extensive experiments verify the effectiveness and practicality of the MC-2PF for different RNN-based prediction models. Compared to state-of-the-art frameworks, the MC-2PF achieves higher prediction accuracy, faster convergence, and stronger adaptiveness.
Keyword ：
Accuracy Accuracy Adaptation models Adaptation models Cloud computing Cloud computing Computational modeling Computational modeling Data models Data models Load modeling Load modeling Load prediction Load prediction multi-edge cooperation multi-edge cooperation personalized federated learning personalized federated learning Predictive models Predictive models Quality of service Quality of service sequential data analysis sequential data analysis Servers Servers Training Training
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| GB/T 7714 | Chen, Zheyi , Jiang, Qingnan , Chen, Lixian et al. MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (6) : 5138-5154 . |
| MLA | Chen, Zheyi et al. "MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 24 . 6 (2025) : 5138-5154 . |
| APA | Chen, Zheyi , Jiang, Qingnan , Chen, Lixian , Chen, Xing , Li, Jie , Min, Geyong . MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (6) , 5138-5154 . |
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As an effective technique to relieve the problem of resource constraints on mobile devices (MDs), the computation offloading utilizes powerful cloud and edge resources to process the computation-intensive tasks of mobile applications uploaded from MDs. In cloud-edge computing, the resources (e.g., cloud and edge servers) that can be accessed by mobile applications may change dynamically. Meanwhile, the parallel tasks in mobile applications may lead to the huge solution space of offloading decisions. Therefore, it is challenging to determine proper offloading plans in response to such high dynamics and complexity in cloud-edge environments. The existing studies often preset the priority of parallel tasks to simplify the solution space of offloading decisions, and thus the proper offloading plans cannot be found in many cases. To address this challenge, we propose a novel real-time and Dependency-aware task Offloading method with Deep Q-networks (DODQ) in cloud-edge computing. In DODQ, mobile applications are first modeled as Directed Acyclic Graphs (DAGs). Next, the Deep Q-Networks (DQN) is customized to train the decision-making model of task offloading, aiming to quickly complete the decision-making process and generate new offloading plans when the environments change, which considers the parallelism of tasks without presetting the task priority when scheduling tasks. Simulation results show that the DODQ can well adapt to different environments and efficiently make offloading decisions. Moreover, the DODQ outperforms the state-of-art methods and quickly reaches the optimal/near-optimal performance.
Keyword ：
Cloud computing Cloud computing Cloud-edge computing Cloud-edge computing Computational modeling Computational modeling deep reinforcement learning deep reinforcement learning dependent and parallel tasks dependent and parallel tasks Heuristic algorithms Heuristic algorithms Mobile applications Mobile applications real-time offloading real-time offloading Real-time systems Real-time systems Servers Servers Task analysis Task analysis
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| GB/T 7714 | Chen, Xing , Hu, Shengxi , Yu, Chujia et al. Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning [J]. | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) : 391-404 . |
| MLA | Chen, Xing et al. "Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning" . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 35 . 3 (2024) : 391-404 . |
| APA | Chen, Xing , Hu, Shengxi , Yu, Chujia , Chen, Zheyi , Min, Geyong . Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) , 391-404 . |
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Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.
Keyword ：
Edge intelligence Edge intelligence federated graph learning federated graph learning neighbor generation neighbor generation semi-supervised learning semi-supervised learning
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| GB/T 7714 | Zhong, Luying , Pi, Yueyang , Chen, Zheyi et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 : 1141-1150 . |
| MLA | Zhong, Luying et al. "SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (2024) : 1141-1150 . |
| APA | Zhong, Luying , Pi, Yueyang , Chen, Zheyi , Yu, Zhengxin , Miao, Wang , Chen, Xing et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 , 1141-1150 . |
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