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学者姓名：冯心欣
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Abstract ：
With the exponential advancement of vehicle networking applications and autonomous driving technology, the demand for efficient and secure autonomous vehicles (AVs) is increasing. AVs require the ability to gather information to navigate complex and ever-changing traffic environments, making effective communication with other vehicles or roadside units (RSUs) crucial for achieving co-awareness. Integrated Sensing and Communication (ISAC) technology emerges as a promising solution for the future of autonomous driving. However, in the dynamic and uncertain real-world road environment, the selection of sensing and communication (SC) functions becomes paramount in enhancing performance. Moreover, ambient noise often disrupts the interaction between vehicles and roadside units, leading to a partial loss of environmental states. To address this challenge, we propose a novel approach for selecting sensing and communication functions, even in the presence of partial loss of environmental states. Specifically, we approximate a partially observable Markov decision process (POMDP) to a complete Markov decision process (MDP) through matrix completion and subsequently utilize deep reinforcement learning (DRL) to solve it. Additionally, we propose a matrix completion algorithm based on the alternating direction method of multipliers (ADMM) with deep unfolding to accurately complete the missing environmental states. Finally, we demonstrate that the proposed method outperforms the other POMDP-based approaches for SC function selection in an ISAC-enabled vehicular network.
Keyword ：
Deep matrix factorization Deep matrix factorization deep unfolding deep unfolding integrated sensing and communication integrated sensing and communication partially observable Markov decision process partially observable Markov decision process vehicular network vehicular network
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| GB/T 7714 | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan et al. Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) : 4933-4945 . |
| MLA | Shen, Xiangyu et al. "Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 74 . 3 (2025) : 4933-4945 . |
| APA | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan , Feng, Xinxin . Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) , 4933-4945 . |
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Multimodal information plays an important role in the advanced Internet of Things (IoT) in the era of 6G, which provides reliable and comprehensive assistance for downstream tasks through further fusion and analysis via federated learning (FL). One of the primary challenges in FL is data heterogeneity, which may lead to domain shifts and sharply different local long-tailed category distribution across nodes. These issues hinder the large-scale deployment of FL in IoT applications equipped with multiple various multimodal sensors due to performance deterioration. In this paper, we propose a novel multimodal fusion framework to tackle the aforementioned coupled problems arising during the cooperative fusion of multimodal information without privacy exposure among decentralized nodes equipped with diverse sensors. Specifically, we introduce a flexible global logit alignment (GLA) method based on multi-view domains. This method enables the fusion of diverse multimodal information with the consideration of domain shifts caused by modality-based data heterogeneity. Furthermore, we propose a novel local angular margin (LAM) scheme, which dynamically adjusts decision boundaries for locally seen categories while preserving global decision boundaries for unseen categories. This effectively mitigates severe model divergence caused by significantly different category distributions. Extensive simulations demonstrate the superiority of the proposed framework, which exhibits significant merits in tackling model degeneration caused by data heterogeneity and enhancing modality-based generalization for heterogeneous scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Federated learning Federated learning
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| GB/T 7714 | Gao, Min , Zheng, Haifeng , Feng, Xinxin et al. Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning [C] . 2025 : 16736-16744 . |
| MLA | Gao, Min et al. "Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning" . (2025) : 16736-16744 . |
| APA | Gao, Min , Zheng, Haifeng , Feng, Xinxin , Tao, Ran . Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning . (2025) : 16736-16744 . |
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Decentralized federated learning (DFL) is a novel distributed machine-learning paradigm where participants collaborate to train machine-learning models without the assistance of the central server. The decentralized framework can effectively overcome the communication bottleneck and single-point-of-failure issues encountered in federated learning (FL). However, most existing DFL methods may ignore the communication resource constraints of the system. This may result in these methods unsuitable for many practical scenarios because the given resource constraints cannot be guaranteed. In this article, we propose a novel DFL, called DFL with adaptive compression ratio (AdapCom-DFL), that can adaptively adjust the compression ratio of transmission data to keep the communication latency within the constraint. Furthermore, we propose a communication network topology pruning approach to reduce communication overhead by pruning poor links with low data rates while ensuring the convergence. Additionally, a power allocation approach is presented to improve the performance by reallocating the power of communication links while complying with the communication energy constraint. Extensive simulation results demonstrate that the proposed AdapCom-DFL with network pruning and power allocation approach achieves better performance and requires less bandwidth under the given resource constraints compared with some existing approaches.
Keyword ：
Decentralized federated learning (DFL) Decentralized federated learning (DFL) network topology pruning network topology pruning power allocation power allocation
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| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Gao, Min et al. Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) : 10739-10753 . |
| MLA | Du, Mengxuan et al. "Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks" . | IEEE INTERNET OF THINGS JOURNAL 11 . 6 (2024) : 10739-10753 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Gao, Min , Feng, Xinxin . Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) , 10739-10753 . |
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This paper explores an intelligent heavy truck solution with integrated sensing, communication, computation, and control (ISCC), capabilities based on the "vehicle-energy-road-cloud" framework for enclosed intermodal railwayroad transport parks. Enabled by communication-sensing convergence networks, the system achieves ubiquitous connectivity across Multi-Agent individual truck intelligence, vehicle coordination intelligence, edge computing, and cloud platforms. The multi-modal sensor fusion of vehicle-energy-road elements ensures comprehensive environmental cognition by combining truck-mounted devices, energy facilities, and roadside sensors. This allows collaborative decisionmaking through distributed in-vehicle and cloud-based analytics. Through intelligent dispatching, precise control commands guide trucks to safely and efficiently complete loading, weighing, vehicle interworking, and other intermodal transport tasks. This human-in-the-loop framework synergizes sensing, communication, computation, and control to fully unlock the potential of new energy heavy trucks, enhancing the safety, accuracy, and efficiency of freight haulage operations in complex enclosed parks.
Keyword ：
and Computation (ISCC) and Computation (ISCC) Communication Communication Integrated Sensing Integrated Sensing Multi-agent reinforcement learning Multi-agent reinforcement learning Multi-modal sensor fusion Multi-modal sensor fusion Vehicle-energy-road-cloud Vehicle-energy-road-cloud
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| GB/T 7714 | Li, Weibin , Zheng, Haifeng , Fang, Jun et al. INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS [J]. | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 , 2024 : 650-654 . |
| MLA | Li, Weibin et al. "INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS" . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 (2024) : 650-654 . |
| APA | Li, Weibin , Zheng, Haifeng , Fang, Jun , Feng, Xinxin , Cheng, Chunyan . INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 , 2024 , 650-654 . |
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Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. In recent years, the emergence of pseudo point clouds has led to an increasing number of 3D object detection tasks introducing this modality, but not every point in the pseudo point cloud generated by depth completion is reliable. In order to better utilize pseudo point clouds in 3D object detection tasks based on point cloud image fusion, we propose the EppNet framework in this paper, which enables the network to learn the anti noise features of pseudo point clouds. In this framework, we use VoxelNet [1] and VirConv Net [2] to extract features from point clouds and pseudo point clouds, respectively. Besides, we utilize a attentive RoI fusion strategy to make fuller use of information from different types of point clouds. Extensive experiments on KITTI, a benchmark for real-world traffic object identification, revealed that EppNet is able to perform favorably in comparison to earlier, well-respected detectors. © 2024 IEEE.
Keyword ：
3D modeling 3D modeling Cloud platforms Cloud platforms Image fusion Image fusion Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Chen, Yuren , Feng, Xinxin , Zheng, Haifeng . EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection [C] . 2024 : 29-32 . |
| MLA | Chen, Yuren et al. "EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection" . (2024) : 29-32 . |
| APA | Chen, Yuren , Feng, Xinxin , Zheng, Haifeng . EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection . (2024) : 29-32 . |
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Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.
Keyword ：
ADMM ADMM deep unfolding network deep unfolding network integrated sensing and communication integrated sensing and communication Orthogonal Time Frequency Space (OTFS) Orthogonal Time Frequency Space (OTFS) target parameter estimation target parameter estimation
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| GB/T 7714 | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin et al. Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Lin, Weizhi et al. "Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin , Chen, Youjia . Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
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Traffic flow is considered as a critical feature of intelligent transportation systems (ITS). Accurately forecasting future vehicular volumes is an effective means of mitigating traffic congestion. However, the nonlinear and complex traffic flow characteristics make the traditional approaches unable to achieve satisfactory prediction performance. Although existing methods based on deep learning models have improved the accuracy of traffic flow prediction, the spatio-temporal features of traffic flow data are still not fully explored. Moreover, existing methods pay little attention to the task of training models in a decentralized environment where data are distributed across multiple clients. To solve the problems mentioned above, we propose a novel network model called Graph Transformer Attention Network (GTAN) for traffic flow prediction, which can effectively extract traffic flow's temporal and spatial characteristics by considering all node locations' information in the traffic networks. Then, we propose a training strategy called Graph Federated Meta-learning (FedGM), solving the problem of topological heterogeneity by combining meta-learning and federated learning, to achieve an optimal initialization model which can quickly adapt to different traffic networks under low communication cost. Finally, the experimental results on a real data set show that the GTAN model has better prediction performance and faster meta-training speed. The model trained by FedGM can quickly adapt to different graph-structured data and achieve high accuracy.
Keyword ：
Adaptation models Adaptation models Correlation Correlation Data models Data models Federated learning Federated learning federated meta-learning federated meta-learning graph transformer networks (GTANs) graph transformer networks (GTANs) Predictive models Predictive models topological heterogeneity topological heterogeneity Traffic flow prediction Traffic flow prediction Training Training Transformers Transformers
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| GB/T 7714 | Feng, Xinxin , Sun, Haoran , Liu, Shunjian et al. Federated Meta-Learning on Graph for Traffic Flow Prediction [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (12) : 19526-19538 . |
| MLA | Feng, Xinxin et al. "Federated Meta-Learning on Graph for Traffic Flow Prediction" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73 . 12 (2024) : 19526-19538 . |
| APA | Feng, Xinxin , Sun, Haoran , Liu, Shunjian , Guo, Junxin , Zheng, Haifeng . Federated Meta-Learning on Graph for Traffic Flow Prediction . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (12) , 19526-19538 . |
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In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.
Keyword ：
ADMM ADMM Atomic norm Atomic norm ISAC ISAC Off-grid target parameter estimation Off-grid target parameter estimation
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| GB/T 7714 | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Ling, Muyao et al. "Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
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Federated learning (FL) has been extensively studied as a means of ensuring data privacy while cooperatively training a global model across decentralized devices. Among various FL approaches, asynchronous federated learning (AFL) has distinct advantages in overcoming the straggler problem via server-side aggregation as soon as it receives a local model. However, AFL still faces several challenges in large-scale real-world applications, such as stale model problems and modality heterogeneity across geographically distributed and industrial devices with different functions. In this article, we propose a multimodal fusion framework for AFL to address the aforementioned problems. Specifically, a novel multilinear block fusion model is designed to fuse various multimodal information, which serves as an enhancement for perceiving and transmitting the important modality and block during local training. An adaptive aggregation strategy is further developed to fully utilize heterogeneous data by allowing the global model to favor the received local model based on both freshness and the importance of the local data. Extensive simulations with different data distributions demonstrate the superiority of the proposed framework in heterogeneity scenarios, which exhibits significant merits in the improvement of modality-based generalization without sacrificing convergence speed and communication consumption.
Keyword ：
Asynchronous federated learning (AFL) Asynchronous federated learning (AFL) block term (BT) decomposition block term (BT) decomposition multimodal fusion multimodal fusion
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| GB/T 7714 | Gao, Min , Zheng, Haifeng , Du, Mengxuan et al. Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) : 14083-14093 . |
| MLA | Gao, Min et al. "Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 12 (2024) : 14083-14093 . |
| APA | Gao, Min , Zheng, Haifeng , Du, Mengxuan , Feng, Xinxin . Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) , 14083-14093 . |
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Federated learning (FL) provides a novel framework to collaboratively train a shared model in a distribution fashion by virtue of a central server. However, FL is inappropriate for a serverless scenario and also suffers from some major drawbacks in Industrial Internet of Things (IIoT) networks, such as unresilience to network failures and communication bottleneck effect. In this article, we propose a novel decentralized federated learning (DFL) approach for IIoT devices to achieve model consensus by exchanging model parameters only with their neighbors rather than a central server. We firstly formulate the problem of model consensus in DFL as a fastest mixing Markov chain problem and then optimize the consensus matrix to improve the convergence rate. Meanwhile, a practical medium access control protocol with time slotted channel hopping is taken into account to implement the proposed approach. Furthermore, we also propose an accumulated update compression method to alleviate communication cost. Finally, extensive simulation results demonstrate that the proposed approach improves accuracy and reduces communication cost especially under the nonindependent identically distribution data distribution.
Keyword ：
Communication compression Communication compression Costs Costs Data models Data models decentralized federated learning (DFL) decentralized federated learning (DFL) fastest mixing Markov chain (FMMC) fastest mixing Markov chain (FMMC) Industrial Internet of Things Industrial Internet of Things Job shop scheduling Job shop scheduling model consensus model consensus Performance evaluation Performance evaluation Servers Servers Training Training
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| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Feng, Xinxin et al. Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2023 , 19 (4) : 6006-6015 . |
| MLA | Du, Mengxuan et al. "Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19 . 4 (2023) : 6006-6015 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Feng, Xinxin , Chen, Youjia , Zhao, Tiesong . Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2023 , 19 (4) , 6006-6015 . |
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