IEEE Transactions on Network Science and Engineering最新文献_第9页

OLMS: A Flexible Online Learning Multi-Path Scheduling Framework 一个灵活的在线学习多路径调度框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-03 DOI: 10.1109/TNSE.2025.3546957

Kechao Cai;Zhuoyue Chen;Jinbei Zhang;John C. S. Lui

{"title":"OLMS: A Flexible Online Learning Multi-Path Scheduling Framework","authors":"Kechao Cai;Zhuoyue Chen;Jinbei Zhang;John C. S. Lui","doi":"10.1109/TNSE.2025.3546957","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546957","url":null,"abstract":"Over the past decade, there has been a tremendous surge in the inter-connectivity among hosts in networks. Many multi-path transport protocols, such as MPTCP, MPQUIC, and MPRDMA, have emerged to facilitate multi-path data transmissions between pairs of hosts. However, existing packet schedulers in these protocols are quite limited as they neglect the stochastic nature inherent in heterogeneous paths, such as, round-trip time and available bandwidth. Moreover, users have diverse requirements; for instance, some prioritize low latency, while others consistently seek to achieve high bandwidth. In this paper, we propose a flexible Online Learning Multi-path Scheduling (OLMS) framework to schedule packets to multiple paths and meet various user-defined requirements by learning the dynamic characteristics of paths in various applications. Specifically, we consider two types of applications, which are 1) <italic>maxRTT constrained</i> and 2) <italic>bandwidth constrained</i>, and use OLMS to schedule packets to satisfy the distinct user-defined requirements. Our theoretical analysis demonstrates that OLMS achieves guarantees with <italic>sublinear</i> regret and <italic>sublinear</i> violation. Furthermore, we implement a prototype of OLMS in MPQUIC and conduct experiments across different scenarios. Our experiments on Mininet show that OLMS enables an 8.42%–18.71% increase in bandwidth utilization in the maxRTT constrained application and negligible violations of user-defined requirements in both applications compared to other schedulers. Additionally, OLMS reduces flow completion times by 4.22%–10.26% compared to other schedulers, all without incurring large overhead.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2277-2291"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rumor Suppression in a Three-Layer Network: A Reinforcement Learning Algorithm 三层网络中的谣言抑制：一种强化学习算法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-03 DOI: 10.1109/TNSE.2025.3546961

Xiaojing Zhong;Jing Zhang;Aojing Wang;Guiyun Liu;Feiqi Deng;Jianhui Wang

{"title":"Rumor Suppression in a Three-Layer Network: A Reinforcement Learning Algorithm","authors":"Xiaojing Zhong;Jing Zhang;Aojing Wang;Guiyun Liu;Feiqi Deng;Jianhui Wang","doi":"10.1109/TNSE.2025.3546961","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546961","url":null,"abstract":"Rumor propagation poses a significant threat to social stability and public order, and controlling its spread can effectively reduce unnecessary panic and misunderstanding. Rumor control is primarily achieved by simulating rumors spread on social networks and disseminating the truth or restricting propagation pathways. However, current studies usually only apply the optimal control theory, which leads to difficulties in coping with complex and stochastic network propagation environments. To address these issues, this paper constructs a three-layer network rumor control model (SICR-3M3W) that considers the dual refutation mechanism and formulates an optimal control problem for this model. Based on the reinforcement learning framework, we design a Proximal Policy Optimization (PPO) algorithm to solve this problem intelligently. Finally, experiments based on a real-world data case are conducted, and the results demonstrate that our three-layer model can effectively simulate the rumor propagation process. Moreover, the designed PPO controller can achieve optimal control outcomes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2292-2307"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protocol-Based Model Predictive Control for Networked Switching Systems With Piecewise-Homogeneous Sojourn Probabilities 分段齐次逗留概率网络交换系统基于协议的模型预测控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-03 DOI: 10.1109/TNSE.2025.3547324

Jun Cheng;Hongjie Pang;Huaicheng Yan;Ju H. Park;Wenhai Qi

{"title":"Protocol-Based Model Predictive Control for Networked Switching Systems With Piecewise-Homogeneous Sojourn Probabilities","authors":"Jun Cheng;Hongjie Pang;Huaicheng Yan;Ju H. Park;Wenhai Qi","doi":"10.1109/TNSE.2025.3547324","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547324","url":null,"abstract":"Networked switching systems, which integrate multiple subsystems controlled by switching signals, play a crucial role in modern cyber-physical applications such as industrial automation and smart grids. However, their performance is often limited by constrained communication bandwidth and complex dynamic interactions. To address these challenges, this paper proposes a protocol-based model predictive control (MPC) framework for networked switching systems with piecewise-homogeneous sojourn probabilities. A dynamically matching mechanism is designed to quantify mode mismatches caused by network-induced uncertainties. Additionally, an adaptive dynamic-memory event-triggered protocol (ADMETP) is developed, which leverages historical data to optimize triggering decisions and dynamically adjusts thresholds to reduce communication overhead while maintaining system stability. Sufficient conditions for mean-square exponential stability are derived using Lyapunov theory, providing rigorous theoretical guarantees. The effectiveness of the approach is validated through simulations of a numerical experiment and an RLC circuit, demonstrating superior resource utilization and control performance compared to existing methods. This work bridges the gap between adaptive resource management and robust control in networked switching systems, offering practical insights for applications with constrained communication resources.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2322-2332"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Privacy-Accuracy Trade-Off Using Adaptive Gradient Clipping in Federated Learning 利用自适应梯度裁剪探索联邦学习中隐私与准确性的权衡

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-28 DOI: 10.1109/TNSE.2025.3546777

Benteng Zhang;Yingchi Mao;Xiaoming He;Ping Ping;Huawei Huang;Jie Wu

{"title":"Exploring the Privacy-Accuracy Trade-Off Using Adaptive Gradient Clipping in Federated Learning","authors":"Benteng Zhang;Yingchi Mao;Xiaoming He;Ping Ping;Huawei Huang;Jie Wu","doi":"10.1109/TNSE.2025.3546777","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546777","url":null,"abstract":"In Differentially Private Federated Learning (DP-FL), gradient clipping can prevent excessive noise from being added to the gradient and ensure that the impact of noise is within a controllable range. However, state-of-the-art methods adopt fixed or imprecise clipping thresholds for gradient clipping, which is not adaptive to the changes in the gradients. This issue can lead to a significant degradation in accuracy while training the global model. To this end, we propose Differential Privacy Federated Adaptive gradient Clipping based on gradient Norm (DP-FedACN). DP-FedACN can calculate the decay rate of the clipping threshold by considering the overall changing trend of the gradient norm. Furthermore, DP-FedACN can accurately adjust the clipping threshold for each training round according to the actual changes in gradient norm, clipping loss, and decay rate. Experimental results demonstrate that DP-FedACN can maintain privacy protection performance similar to that of DP-FedAvg under member inference attacks and model inversion attacks. DP-FedACN significantly outperforms DP-FedAGNC and DP-FedDDC in privacy protection metrics. Additionally, the test accuracy of DP-FedACN is approximately 2.61%, 1.01%, and 1.03% higher than the other three baseline methods, respectively. DP-FedACN can improve the global model training accuracy while ensuring the privacy protection of the model. All experimental results demonstrate that the proposed DP-FedACN can help find a fine-grained privacy-accuracy trade-off in DP-FL.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2254-2265"},"PeriodicalIF":6.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and Adaptive CUR Matrix Decomposition for Flexible Compression of Network Monitoring Data 基于有效自适应CUR矩阵分解的网络监测数据灵活压缩

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-28 DOI: 10.1109/TNSE.2025.3546687

Jigang Wen;Shiqin Wang;Kun Xie;Jiazheng Tian;Yixuan Wang

{"title":"Efficient and Adaptive CUR Matrix Decomposition for Flexible Compression of Network Monitoring Data","authors":"Jigang Wen;Shiqin Wang;Kun Xie;Jiazheng Tian;Yixuan Wang","doi":"10.1109/TNSE.2025.3546687","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546687","url":null,"abstract":"Network-wide monitoring is indispensable for a variety of network applications. However, as network sizes increase and the demand for fine-grained, continuous measurements grows, the challenges associated with storing and transmitting such data intensify. Recent studies have shown that network-wide monitoring data exhibits a low-rank structure, which can be exploited using matrix decomposition techniques for compression. This paper presents a compression algorithm for low-rank matrices based on CUR decomposition, which offers enhanced interpretability compared to SVD-based compression. Existing CUR solutions, however, lack the capability for fast and flexible compression that can dynamically adjust to matrix size requirements while preserving maximal approximation accuracy. We address the challenges associated with CUR row and column selection by formulating it as a deterministic CUR matrix decomposition problem, involving a selection matrix <inline-formula><tex-math>$mathbf{W}$</tex-math></inline-formula>. To achieve rapid compression, we propose an algorithm that effectively accelerates the process of solving for the parameter matrix <inline-formula><tex-math>$mathbf{W}$</tex-math></inline-formula>. Our approach reveals that the vectors in <inline-formula><tex-math>$mathbf{W}$</tex-math></inline-formula> indicate the importance of each row and column in forming the respective row and column subspaces. Leveraging this insight, we develop a flexible compression algorithm based on the sorted vectors in the selection matrix <inline-formula><tex-math>$mathbf{W}$</tex-math></inline-formula>. This method not only ensures the required compression ratio but also maintains maximal approximation accuracy. Extensive experiments on both synthesized and real data demonstrate that our algorithm can deliver fast and precise matrix compression, aligning with the desired compression ratio.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2231-2242"},"PeriodicalIF":6.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cellular Traffic Prediction via Byzantine-Robust Asynchronous Federated Learning 基于拜占庭鲁棒异步联邦学习的蜂窝流量预测

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-27 DOI: 10.1109/TNSE.2025.3545912

Hui Ma;Kai Yang;Yang Jiao

{"title":"Cellular Traffic Prediction via Byzantine-Robust Asynchronous Federated Learning","authors":"Hui Ma;Kai Yang;Yang Jiao","doi":"10.1109/TNSE.2025.3545912","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3545912","url":null,"abstract":"Network traffic prediction plays a crucial role in intelligent network operation. Traditional prediction methods often rely on centralized training, necessitating the transfer of vast amounts of traffic data to a central server. This approach can lead to latency and privacy concerns. To address these issues, federated learning integrated with differential privacy has emerged as a solution to improve data privacy and model robustness in distributed settings. Nonetheless, existing federated learning protocols are vulnerable to Byzantine attacks, which may significantly compromise model robustness. Developing a robust and privacy-preserving prediction model in the presence of Byzantine clients remains a significant challenge. To this end, we propose an asynchronous differential federated learning framework based on distributionally robust optimization. The proposed framework utilizes multiple clients to train the prediction model collaboratively with local differential privacy. In addition, regularization techniques have been employed to further improve the Byzantine robustness of the models. We have conducted extensive experiments on three real-world datasets, and the results elucidate that our proposed distributed algorithm can achieve superior performance over existing methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2402-2414"},"PeriodicalIF":6.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Leader-Following Formation Control of Networked Mobile Robots via Global Orientation Estimation 基于全局方向估计的网络化移动机器人分布式leader - follower群体控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-27 DOI: 10.1109/TNSE.2025.3545119

Siqi Wang;Heng Wang;Weiwei Che;Qing Li

{"title":"Distributed Leader-Following Formation Control of Networked Mobile Robots via Global Orientation Estimation","authors":"Siqi Wang;Heng Wang;Weiwei Che;Qing Li","doi":"10.1109/TNSE.2025.3545119","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3545119","url":null,"abstract":"This article proposes a novel distributed leader-following formation control strategy for multiple networked mobile robots, utilizing the relative position measurements among robots. In particular, the leader is assumed to have the same kinematics as the followers and all the robots do not rely on orientation measurements. Firstly, a global orientation estimation law is proposed in the sense that the followers' orientation information is estimated in the leader's reference frame, only based on the leader's orientation estimation and relative bearing information. Secondly, since the leader is not directly connected to all the followers, a new distributed state observer is designed for each follower to estimate the leader's states. Especially, the designed observer not only removes the algebraic loops issue but also eliminates the requirement for the leader's acceleration information. Furthermore, a distributed formation control law is proposed by incorporating the previous estimations, and it is proved that the closed-loop system consisting of the observer and controller is asymptotically stable. Finally, simulation results validate the effectiveness and superiority of the proposed method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2135-2150"},"PeriodicalIF":6.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Federated Learning in Wireless Networks With Incremental Model Quantization and Uploading 基于增量模型量化和上传的无线网络高效联邦学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-26 DOI: 10.1109/TNSE.2025.3546333

Zheng Qin;Gang Feng;YiJing Liu;Takshing P. Yum;Fei Wang;Jun Wang

{"title":"Efficient Federated Learning in Wireless Networks With Incremental Model Quantization and Uploading","authors":"Zheng Qin;Gang Feng;YiJing Liu;Takshing P. Yum;Fei Wang;Jun Wang","doi":"10.1109/TNSE.2025.3546333","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546333","url":null,"abstract":"Federated Learning (FL) has been widely recognized as a promising promoter for future intelligent wireless networks, by collaboratively training a global machine learning (ML) model in a privacy-preserving manner. However, the transmission of large-scale models between clients and servers is susceptible to limited communication resources. Recently proposed model quantization can reduce communication costs by compressing the amount of model data to be transmitted. These methods need to be modified when used in wireless networks with rapidly changing radio channels. In this paper, we propose a federated learning scheme with incremental model quantization and uploading mechanism, called Fed_IQ. Specifically, individual clients quantize the local model parameters to derive the base and incremental model parameters. The base model is first uploaded, while the incremental model is uploaded when the wireless link is sufficiently good. The quantization levels are also adapted to the instantaneous channel states. The server then uses only the base model or combines the base and incremental model to aggregate a more accurate global model. Experimental results show our proposed Fed_IQ can significantly reduce transmission delay and improve model accuracy in a wireless network compared with a number of known state-of-the-art algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2217-2230"},"PeriodicalIF":6.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large-Scale Group Opinion Evolution With Coexistence of Influential Individuals and Strongly Organized Groups Based on Mean Field Games 基于平均场博弈的有影响力个体与强组织群体共存的大规模群体意见演化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-26 DOI: 10.1109/TNSE.2025.3546295

Lu Ren;Yuxin Jin;Wang Yao;Xiao Zhang;Guanghui Jiao

{"title":"Large-Scale Group Opinion Evolution With Coexistence of Influential Individuals and Strongly Organized Groups Based on Mean Field Games","authors":"Lu Ren;Yuxin Jin;Wang Yao;Xiao Zhang;Guanghui Jiao","doi":"10.1109/TNSE.2025.3546295","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546295","url":null,"abstract":"With the vigorous development of online media, the prevalence of key opinion leaders and water armies has led to unexpected evolutions of users' opinions. Therefore, it is valuable and interesting to investigate the opinion evolution problem for large-scale groups with the coexistence of influential individuals and strongly organized groups. For the above problem, based on the mean-field game theory, this article innovatively proposes a multi-leader multi-population-follower Stackelberg mean field game (MLMPF-SMFG) model to describe the opinion evolution scenario, in which influential individuals are regarded as leaders, normal and strongly organized groups are regarded as follower populations. Moreover, for generality, the types of strongly organized individuals are classified into three typical types: propagandists, spies, and neutrals. Then, the optimal strategies are derived via the adjoint method and solved by forward–backward stochastic differential equations. Sufficient conditions for the existence and uniqueness of the Stackelberg equilibrium (SE) are given, and the approximate SE of the finite system is proven. Finally, simulation experiments on the opinion evolutions of two influential individuals and two ordinary groups are performed to demonstrate the feasibility and effectiveness of the proposed MLMPF-SMFG model.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2202-2216"},"PeriodicalIF":6.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated Deep Reinforcement Learning for Multimodal Content Caching in Edge-Cloud Networks 边缘云网络中多模态内容缓存的联邦深度强化学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-26 DOI: 10.1109/TNSE.2025.3545924

Weijia Feng;Xinyu Zuo;Ruojia Zhang;Yichen Zhu;Chenyang Wang;Jia Guo;Chuan Sun

{"title":"Federated Deep Reinforcement Learning for Multimodal Content Caching in Edge-Cloud Networks","authors":"Weijia Feng;Xinyu Zuo;Ruojia Zhang;Yichen Zhu;Chenyang Wang;Jia Guo;Chuan Sun","doi":"10.1109/TNSE.2025.3545924","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3545924","url":null,"abstract":"Edge caching presents a promising avenue for mitigating backbone network congestion by strategically caching frequently accessed content at the network periphery. As most current edge caching solutions are designed for single-modal content requests, they cannot deal with the increasing volume of multi-modal content requests. In this study, we investigate the issue of multimodal content caching in edge-cloud networks. Firstly, we establish a heterogeneous edge-cloud network adept at caching multimodal content proximate to end-users to facilitate expeditious content delivery. By leveraging latent representations of multimodal content, we identify distinct user request modalities for multimodal content. Subsequently, we formulate caching replacement operations as a Markov Decision Process (MDP) aimed at minimizing user-content access latency. Moreover, we propose a decentralized multimodal content caching framework at the network edge based on federated deep reinforcement learning. This framework affords distributed decision-making and learning capabilities, thereby alleviating the strain on centralized resources and augmenting caching efficacy. To demonstrate the efficacy of our proposed framework, we conduct comprehensive experiments utilizing the Noah-Wukong dataset. Experimental results provide evidence that our framework reduces average latency by up to 10% compared to traditional methods, highlighting its proficiency in enhancing cache performance in edge-cloud networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2188-2201"},"PeriodicalIF":6.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0