IEEE Transactions on Mobile Computing最新文献_第8页

Robust Device-Free mmWave Sensing With Specular Reflection Interference Mitigation 具有镜面反射干扰抑制的鲁棒无器件毫米波传感

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-03 DOI: 10.1109/TMC.2025.3538112

Yulin Liu;Jie Wang;Qinghua Gao;Miao Pan;Yuguang Fang

{"title":"Robust Device-Free mmWave Sensing With Specular Reflection Interference Mitigation","authors":"Yulin Liu;Jie Wang;Qinghua Gao;Miao Pan;Yuguang Fang","doi":"10.1109/TMC.2025.3538112","DOIUrl":"https://doi.org/10.1109/TMC.2025.3538112","url":null,"abstract":"Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5749-5764"},"PeriodicalIF":7.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255661","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

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning 基于进化多目标深度强化学习的地面移动用户空中可靠协同通信

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-29 DOI: 10.1109/TMC.2025.3536093

Geng Sun;Jian Xiao;Jiahui Li;Jiacheng Wang;Jiawen Kang;Dusit Niyato;Shiwen Mao

{"title":"Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning","authors":"Geng Sun;Jian Xiao;Jiahui Li;Jiacheng Wang;Jiawen Kang;Dusit Niyato;Shiwen Mao","doi":"10.1109/TMC.2025.3536093","DOIUrl":"https://doi.org/10.1109/TMC.2025.3536093","url":null,"abstract":"Autonomous aerial vehicles (AAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a AAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a AAV-enabled virtual antenna array to improve transmission performance from the AAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the AAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5731-5748"},"PeriodicalIF":7.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255629","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

Joint Service Caching and Resource Allocation Over Different Timescales in Satellite Edge Computing Networks 卫星边缘计算网络中不同时间尺度的联合服务缓存与资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-28 DOI: 10.1109/TMC.2025.3534779

Han Hu;Kaifeng Song;Cheng Zhan;Rongfei Fan;Jian Yang

{"title":"Joint Service Caching and Resource Allocation Over Different Timescales in Satellite Edge Computing Networks","authors":"Han Hu;Kaifeng Song;Cheng Zhan;Rongfei Fan;Jian Yang","doi":"10.1109/TMC.2025.3534779","DOIUrl":"https://doi.org/10.1109/TMC.2025.3534779","url":null,"abstract":"The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5649-5664"},"PeriodicalIF":7.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255627","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

Survey, Design and Evaluation of TGT-HC: A Time-Aware Shaper MAC for Wireless TSN TGT-HC：无线TSN的时间感知整形MAC的研究、设计与评价

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-28 DOI: 10.1109/TMC.2025.3535413

Raymond J. Jayabal;David Tung Chong Wong;Lee Kee Goh;Xiaojuan Zhang;Chin Ming Pang;Sumei Sun

引用次数: 0

PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud PAC-MC：一种有效的基于密码的时间序列感知媒体云访问控制框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-28 DOI: 10.1109/TMC.2025.3534861

Haiyan Wang;Penghui Liu;Xiaoxiong Zhong;Fucai Luo;Bin Xiao;Yuanyuan Yang

{"title":"PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud","authors":"Haiyan Wang;Penghui Liu;Xiaoxiong Zhong;Fucai Luo;Bin Xiao;Yuanyuan Yang","doi":"10.1109/TMC.2025.3534861","DOIUrl":"https://doi.org/10.1109/TMC.2025.3534861","url":null,"abstract":"Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5632-5648"},"PeriodicalIF":7.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255664","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

MWRS: A MAB-Based Worker Recruitment Scheme With Tripartite Stackelberg Game for Reliable Mobile Crowdsensing MWRS：一种基于mab的基于三方Stackelberg博弈的可靠移动众测员工招聘方案

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-28 DOI: 10.1109/TMC.2025.3535567

Yan Ouyang;Feng Zeng;Neal N. Xiong;Anfeng Liu;Witold Pedrycz

{"title":"MWRS: A MAB-Based Worker Recruitment Scheme With Tripartite Stackelberg Game for Reliable Mobile Crowdsensing","authors":"Yan Ouyang;Feng Zeng;Neal N. Xiong;Anfeng Liu;Witold Pedrycz","doi":"10.1109/TMC.2025.3535567","DOIUrl":"https://doi.org/10.1109/TMC.2025.3535567","url":null,"abstract":"Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers’ reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5665-5680"},"PeriodicalIF":7.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255625","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

Privacy-Preserving Stable Data Trading for Unknown Market Based on Blockchain 基于区块链的未知市场保隐私稳定数据交易

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-27 DOI: 10.1109/TMC.2025.3534201

Qingyong Deng;Qinghua Zuo;Zhetao Li;Haolin Liu;Yong Xie

{"title":"Privacy-Preserving Stable Data Trading for Unknown Market Based on Blockchain","authors":"Qingyong Deng;Qinghua Zuo;Zhetao Li;Haolin Liu;Yong Xie","doi":"10.1109/TMC.2025.3534201","DOIUrl":"https://doi.org/10.1109/TMC.2025.3534201","url":null,"abstract":"Crowdsensing Data Trading (CDT) has emerged as a novel data trading paradigm, where market stability is crucial during the transaction matching process. However, most existing CDT systems usually assume that the preferences of both parties are known and the third-party trading platform is trustworthy, which is impractical in real-world scenarios and leads to significant challenges in reliability and privacy preservation. To address these challenges, we propose a Privacy-Preserving and Stable Data Trading for Unknown Market based on Blockchain and Bilateral Reputation (PPSDT-UMBBR) scheme in the decentralized CDT system. First, a privacy-preserving bilateral preference initialization method is designed to achieve the initial matching of buyers and sellers without exposing their location and attribute privacy. Then, a stable matching method based on dynamic bilateral preference updating is proposed, integrating Differential Privacy, Stable matching theory, and a strategy based on Asymmetric Bilateral Preferences with Multi-Armed Bandits (DPS-ABPMAB). Finally, we theoretically analyze the security and prove that the market outcome is <inline-formula><tex-math>$delta$</tex-math></inline-formula>-stable. Furthermore, compared to other benchmark methods based on real datasets, our proposed DPS-ABPMAB algorithm improves the average accumulative reward by at least 4.22%, and reduces the average accumulative regret and the mean evaluation error rate by at least 66.86% and 7.35%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5615-5631"},"PeriodicalIF":7.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255738","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 ENDC Optimization ENDC优化的联邦深度强化学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-27 DOI: 10.1109/TMC.2025.3534661

Adrian Martin;Isabel de-la-Bandera;Adriano Mendo;Jose Outes;Juan Ramiro;Raquel Barco

{"title":"Federated Deep Reinforcement Learning for ENDC Optimization","authors":"Adrian Martin;Isabel de-la-Bandera;Adriano Mendo;Jose Outes;Juan Ramiro;Raquel Barco","doi":"10.1109/TMC.2025.3534661","DOIUrl":"https://doi.org/10.1109/TMC.2025.3534661","url":null,"abstract":"5G New Radio (NR) network deployment in Non-Stand Alone (NSA) mode means that 5G networks rely on the control plane of existing Long Term Evolution (LTE) modules for control functions, while 5G modules are only dedicated to the user plane tasks, which could also be carried out by LTE modules simultaneously. The first deployments of 5G networks are essentially using this technology. These deployments enable what is known as E-UTRAN NR Dual Connectivity (ENDC), where a user establish a 5G connection simultaneously with a pre-existing LTE connection to boost their data rate. In this paper, a single Federated Deep Reinforcement Learning (FDRL) agent for the optimization of the event that triggers the dual connectivity between LTE and 5G is proposed. First, single Deep Reinforcement Learning (DRL) agents are trained in isolated cells. Later, these agents are merged into a unique global agent capable of optimizing the whole network with Federated Learning (FL). This scheme of training single agents and merging them also makes feasible the use of dynamic simulators for this type of learning algorithm and parameters related to mobility, by drastically reducing the number of possible combinations resulting in fewer simulations. The simulation results show that the final agent is capable of achieving a tradeoff between dropped calls and the user throughput to achieve global optimum without the need for interacting with all the cells for training.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5525-5535"},"PeriodicalIF":7.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Content-Aware Joint Knob Configuration and Resource Allocation for Edge Video Analytics 面向边缘视频分析的内容感知联合旋钮配置和资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-24 DOI: 10.1109/TMC.2025.3533596

Tong Bai;Bo Hou;Zhipeng Wang;Dong Liu;Arumugam Nallanathan

{"title":"Content-Aware Joint Knob Configuration and Resource Allocation for Edge Video Analytics","authors":"Tong Bai;Bo Hou;Zhipeng Wang;Dong Liu;Arumugam Nallanathan","doi":"10.1109/TMC.2025.3533596","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533596","url":null,"abstract":"Characterized by its ease of low-latency response, edge computing is capable of supporting real-time video analytics applications, constituting an edge video analytics paradigm, where the joint knob configuration and network scheduling design has drawn ever-escalating research attention. However, the potential of edge video analytics has not been fully exploited, owing to the limitations of the state-of-the-art as follows. i) The eminent impact of video content on accuracy performance has been ignored. ii) The variables that can be tuned are not fully considered in scheduling. iii) The heuristic algorithm-based solutions are far from the optimal. To fill in this gap, in this paper, we conceive a content-aware joint knob configuration and resource allocation scheme for edge video analytics. Concretely, fed with the features extracted from the video content, a deep neural network (DNN)-based predictor is proposed to predict the configuration-accuracy performance in a real-time manner. With an aid of the predictive results, we formulate an accuracy-maximization problem as an integer programming problem, by optimizing the variables, including resolution, frame rate, video analytic model, network bandwidth, and computational resource subject to the latency constraints. To solve this problem in an efficient manner, we devise a novel low-complexity dynamic programming method. Simulation results verify the efficiency of our content-aware joint knob configuration and resource allocation scheme. Quantitatively, a 3.3% gap is attained towards the upper bound in terms of the accuracy in an object detection scenario, relying on the scheme proposed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5536-5550"},"PeriodicalIF":7.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929767","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

Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach 异构联邦边缘学习的快速收敛：一种自适应聚类数据共享方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-24 DOI: 10.1109/TMC.2025.3533566

Gang Hu;Yinglei Teng;Nan Wang;Zhu Han

{"title":"Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach","authors":"Gang Hu;Yinglei Teng;Nan Wang;Zhu Han","doi":"10.1109/TMC.2025.3533566","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533566","url":null,"abstract":"Federated Edge Learning (FEL) emerges as a pioneering distributed machine learning paradigm for the 6 G Hyper-Connectivity, harnessing data from the IoT devices while upholding data privacy. However, current FEL algorithms struggle with non-independent and non-identically distributed (non-IID) data, leading to elevated communication costs and compromised model accuracy. To address these statistical imbalances, we introduce a clustered data sharing framework, mitigating data heterogeneity by selectively sharing partial data from cluster heads to trusted associates through sidelink-aided multicasting. The collective communication pattern is integral to FEL training, where both cluster formation and the efficiency of communication and computation impact training latency and accuracy simultaneously. To tackle the strictly coupled data sharing and resource optimization, we decompose the optimization problem into the clients clustering and effective data sharing subproblems. Specifically, a distribution-based adaptive clustering algorithm (DACA) is devised basing on three deductive cluster forming conditions, which ensures the maximum sharing yield. Meanwhile, we design a stochastic optimization based joint computed frequency and shared data volume optimization (JFVO) algorithm, determining the optimal resource allocation with an uncertain objective function. The experiments show that the proposed framework facilitates FEL on non-IID datasets with faster convergence rate and higher model accuracy in a resource-limited environment.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5342-5356"},"PeriodicalIF":7.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918729","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