IEEE Transactions on Mobile Computing最新文献_第9页

FLrce: Resource-Efficient Federated Learning With Early-Stopping Strategy FLrce：采用提前停止策略的资源节约型联合学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-21 DOI: 10.1109/TMC.2024.3447000

Ziru Niu;Hai Dong;A. K. Qin;Tao Gu

{"title":"FLrce: Resource-Efficient Federated Learning With Early-Stopping Strategy","authors":"Ziru Niu;Hai Dong;A. K. Qin;Tao Gu","doi":"10.1109/TMC.2024.3447000","DOIUrl":"10.1109/TMC.2024.3447000","url":null,"abstract":"Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a \u0000<bold>r\u0000elationship-based \u0000<bold>c\u0000lient selection and \u0000<bold>e\u0000arly-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14514-14529"},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180591","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

Multi-Objective Optimization for Multi-UAV-Assisted Mobile Edge Computing 多无人机辅助移动边缘计算的多目标优化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-21 DOI: 10.1109/TMC.2024.3446819

Geng Sun;Yixian Wang;Zemin Sun;Qingqing Wu;Jiawen Kang;Dusit Niyato;Victor C. M. Leung

{"title":"Multi-Objective Optimization for Multi-UAV-Assisted Mobile Edge Computing","authors":"Geng Sun;Yixian Wang;Zemin Sun;Qingqing Wu;Jiawen Kang;Dusit Niyato;Victor C. M. Leung","doi":"10.1109/TMC.2024.3446819","DOIUrl":"10.1109/TMC.2024.3446819","url":null,"abstract":"Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computation-intensive and delay-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we consider a multi-UAV-assisted MEC system. Based on this system, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total number of offloaded tasks. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach. The problem is split into three components to cope with the coupling of these decision variables, and then solved individually to obtain the corresponding decisions. Specifically, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared with the other benchmark methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14803-14820"},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180589","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

Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays 通过多跳中继在车载网络中实现远距离点云注册

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-21 DOI: 10.1109/TMC.2024.3446828

Zhenxi Wang;Hongzi Zhu;Yunxiang Cai;Quan Liu;Shan Chang;Liang Zhang;Minyi Guo

{"title":"Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays","authors":"Zhenxi Wang;Hongzi Zhu;Yunxiang Cai;Quan Liu;Shan Chang;Liang Zhang;Minyi Guo","doi":"10.1109/TMC.2024.3446828","DOIUrl":"10.1109/TMC.2024.3446828","url":null,"abstract":"Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. To the best of our knowledge, LoRaPCR is the first solution to achieve multi-vehicle point cloud long-range registration. Results of extensive experiments demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43\u0000<inline-formula><tex-math>${}^{circ }$</tex-math></inline-formula>\u0000, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14821-14833"},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180597","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

Risk-Aware Reinforcement Learning-Based Federated Learning for IoV Systems 面向物联网系统的基于风险意识强化学习的联合学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-21 DOI: 10.1109/TMC.2024.3447034

Xiaozhen Lu;Zhibo Liu;Yuhan Chen;Liang Xiao;Wei Wang;Qihui Wu

{"title":"Risk-Aware Reinforcement Learning-Based Federated Learning for IoV Systems","authors":"Xiaozhen Lu;Zhibo Liu;Yuhan Chen;Liang Xiao;Wei Wang;Qihui Wu","doi":"10.1109/TMC.2024.3447034","DOIUrl":"10.1109/TMC.2024.3447034","url":null,"abstract":"Federated learning (FL) that improves data privacy reduces the computational overhead for Internet of Vehicles (IoV) systems but has difficulty in defending against selfish attacks due to the restricted quality of service requirements and the high mobility of vehicles. In this paper, we design a risk-aware hierarchical reinforcement learning-based FL framework for IoV to resist selfish attacks. By designing a two-level hierarchical policy selection module that consists of two deep neural networks, this framework divides the training policy into two sub-policies, i.e., the selection of FL participants and the corresponding local training data size, which are chosen based on the previous training performance and vehicle participation performance. This framework designs a risk-aware safety guide to avoid dangerous states such as local task failure resulting from risky training policies. Specifically, the guide uses a warning signal to evaluate the short-term risk of each state-action pair, applies an R-network to estimate the long-term risks for modifying the chosen training policy, and designs a punishment function for the modified training policy to revise the immediate reward to further enhance the safe exploration. We analyze the convergence performance and computational complexity of our scheme. Experimental results on MNIST, CIFAR-10, and Stanford Cars datasets verify the effectiveness of our scheme, including the global model accuracy, training latency, detection success rate, and convergence speed compared with the benchmarks FedAvg, MFL, DQNPS, and SHRL.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14672-14688"},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180598","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

P2CEFL: Privacy-Preserving and Communication Efficient Federated Learning With Sparse Gradient and Dithering Quantization P2CEFL：利用稀疏梯度和抖动量化进行隐私保护和通信高效的联合学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-20 DOI: 10.1109/TMC.2024.3445957

Gang Wang;Qi Qi;Rui Han;Lin Bai;Jinho Choi

{"title":"P2CEFL: Privacy-Preserving and Communication Efficient Federated Learning With Sparse Gradient and Dithering Quantization","authors":"Gang Wang;Qi Qi;Rui Han;Lin Bai;Jinho Choi","doi":"10.1109/TMC.2024.3445957","DOIUrl":"https://doi.org/10.1109/TMC.2024.3445957","url":null,"abstract":"Federated learning (FL) offers a promising framework for obtaining a global model by aggregating trained parameters from participating clients without transmitting their local private data. To further enhance privacy, differential privacy (DP)-based FL can be considered, wherein certain amounts of noise are added to the transmitting parameters, inevitably leading to a deterioration in communication efficiency. In this paper, we propose a novel Privacy-Preserving and Communication Efficient Federated Learning (P2CEFL) algorithm to reduce communication overhead under DP guarantee, utilizing sparse gradient and dithering quantization. Through gradient sparsification, the upload overhead for clients decreases considerably. Additionally, a subtractive dithering approach is employed to quantize sparse gradient, further reducing the bits for communication. We conduct theoretical analysis on privacy protection and convergence to verify the effectiveness of the proposed algorithm. Extensive numerical simulations show that the P2CEFL algorithm can achieve a similar level of model accuracy and significantly reduce communication costs compared to existing conventional DP-based FL methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14722-14736"},"PeriodicalIF":7.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636478","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

FedASA: A Personalized Federated Learning With Adaptive Model Aggregation for Heterogeneous Mobile Edge Computing FedASA：针对异构移动边缘计算的具有自适应模型聚合功能的个性化联合学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-20 DOI: 10.1109/TMC.2024.3446271

Dongshang Deng;Xuangou Wu;Tao Zhang;Xiangyun Tang;Hongyang Du;Jiawen Kang;Jiqiang Liu;Dusit Niyato

{"title":"FedASA: A Personalized Federated Learning With Adaptive Model Aggregation for Heterogeneous Mobile Edge Computing","authors":"Dongshang Deng;Xuangou Wu;Tao Zhang;Xiangyun Tang;Hongyang Du;Jiawen Kang;Jiqiang Liu;Dusit Niyato","doi":"10.1109/TMC.2024.3446271","DOIUrl":"https://doi.org/10.1109/TMC.2024.3446271","url":null,"abstract":"Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and efficient FL method, termed FedASA, which can address the challenge of statistical heterogeneity in resource-constrained scenarios by determining the shared architecture adaptively. In FedASA, we first present a cell-wised shared architecture selection strategy, which can adaptively construct the shared architecture for each device. We then design a cell-based aggregation algorithm for aggregating heterogeneous shared architectures. In addition, we provide a theoretical analysis of the federated error bound, which provides a theoretical guarantee for the fairness. At the same time, we prove the convergence of FedASA at the first-order stationary point. We evaluate the performance of FedASA through extensive simulation and experiments. Experimental results in cross-location scenarios show that FedASA outperformed the state-of-the-art approaches, improving accuracy by up to 13.27% with better fairness and faster convergence and communication requirement has been reduced by 81.49%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14787-14802"},"PeriodicalIF":7.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636477","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

StreamingTag: A Scalable Piracy Tracking Solution for Mobile Streaming Services StreamingTag：移动流媒体服务的可扩展盗版跟踪解决方案

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-19 DOI: 10.1109/TMC.2024.3445411

Fan Dang;Xinqi Jin;Qi-An Fu;Lingkun Li;Guanyan Peng;Xinlei Chen;Kebin Liu;Yunhao Liu

{"title":"StreamingTag: A Scalable Piracy Tracking Solution for Mobile Streaming Services","authors":"Fan Dang;Xinqi Jin;Qi-An Fu;Lingkun Li;Guanyan Peng;Xinlei Chen;Kebin Liu;Yunhao Liu","doi":"10.1109/TMC.2024.3445411","DOIUrl":"10.1109/TMC.2024.3445411","url":null,"abstract":"Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14530-14543"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180599","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

Lyapunov-Guided Offloading Optimization Based on Soft Actor-Critic for ISAC-Aided Internet of Vehicles 基于软行为批判的 Lyapunov 引导卸载优化，用于 ISAC 辅助车联网

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-19 DOI: 10.1109/TMC.2024.3445350

Yonghui Liang;Huijun Tang;Huaming Wu;Yixiao Wang;Pengfei Jiao

{"title":"Lyapunov-Guided Offloading Optimization Based on Soft Actor-Critic for ISAC-Aided Internet of Vehicles","authors":"Yonghui Liang;Huijun Tang;Huaming Wu;Yixiao Wang;Pengfei Jiao","doi":"10.1109/TMC.2024.3445350","DOIUrl":"10.1109/TMC.2024.3445350","url":null,"abstract":"Due to numerous computation-intensive and delay-sensitive tasks in the Internet of Vehicles (IoV), Vehicular Edge Computing (VEC) is increasingly playing a crucial role as a key solution in the IoV. However, how to concurrently enhance communication quality and reduce the cost of latency and energy has emerged as a critical challenge in VEC. To tackle the above problem, we propose a Lyapunov-guided offloading based on the Soft Actor-Critic (SAC) algorithm, named LySAC, to minimize the average cost of the Integrated Sensing and Communications (ISAC) technology-aided IoV, where ISAC technology can effectively improve the communication quality by harnessing high-frequency waveforms to seamlessly integrate communication and sensing functionalities. First, we model the offloading process of ISAC-Aided IoV as an optimization problem of the joint cost of delay and energy with long-term energy consumption and queue stability. Then we formulate the optimization problem as a Lyapunov optimization and utilize the SAC method to find the optimal offloading decisions. Finally, we conduct extensive experiments and the results demonstrate the effectiveness and superiority of the proposed LySAC in minimizing total cost while maintaining queue stability and meeting long-term energy requirements compared with other several baseline schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14708-14721"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180601","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

Learning to Optimize State Estimation in Multi-Agent Reinforcement Learning-Based Collaborative Detection 在基于强化学习的多代理协作检测中学习优化状态估计

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-19 DOI: 10.1109/TMC.2024.3445583

Tianlong Zhou;Tianyi Shi;Hongye Gao;Weixiong Rao

{"title":"Learning to Optimize State Estimation in Multi-Agent Reinforcement Learning-Based Collaborative Detection","authors":"Tianlong Zhou;Tianyi Shi;Hongye Gao;Weixiong Rao","doi":"10.1109/TMC.2024.3445583","DOIUrl":"10.1109/TMC.2024.3445583","url":null,"abstract":"In this paper, we study the collaborative detection problem in a multi-agent environment. By exploiting onboard range-bearing sensors, mobile agents make sequential control decisions such as moving directions to gather information of movable targets. To estimate target states, i.e., target location and velocity, the classic works such as Kalman Filter (KF) and Extended Kalman Filter (EKF) impractically assume that the underlying state space model is fully known, and some recent learning-based works, i.e., KalmanNet, estimate target states alone but without estimation uncertainty, and cannot make robust control decision. To tackle such issues, we first propose a neural network-based state estimator, namely T\u0000<underline>W\u0000o-phase K\u0000<underline>AL\u0000ma\u0000<underline>n\u0000 Filter with \u0000<underline>U\u0000ncertainty quan\u0000<underline>T\u0000ification (WALNUT), to explicitly give both target states and estimation uncertainty. The developed multi-agent reinforcement learning (MARL) model then takes the learned target states and uncertainty as input and makes robust actions to track movable targets. Our extensive experiments demonstrate that our work outperforms the state-of-the-art by higher tracking ability and lower localization error.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14330-14343"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180600","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

Learn to Collaborate in MEC: An Adaptive Decentralized Federated Learning Framework 在 MEC 中学习协作：自适应分散式联盟学习框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-08-19 DOI: 10.1109/TMC.2024.3439588

Yatong Wang;Zhongyi Wen;Yunjie Li;Bin Cao

{"title":"Learn to Collaborate in MEC: An Adaptive Decentralized Federated Learning Framework","authors":"Yatong Wang;Zhongyi Wen;Yunjie Li;Bin Cao","doi":"10.1109/TMC.2024.3439588","DOIUrl":"https://doi.org/10.1109/TMC.2024.3439588","url":null,"abstract":"Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14071-14084"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595897","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