IEEE Transactions on Mobile Computing最新文献_第2页

LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning lefl：联邦学习的轻量级贡献评估方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-25 DOI: 10.1109/TMC.2025.3545140

Jingjing Guo;Jiaxing Li;Zhiquan Liu;Yupeng Xiong;Yong Ma;Athanasios V. Vasilakos;Xinghua Li;Jianfeng Ma

{"title":"LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning","authors":"Jingjing Guo;Jiaxing Li;Zhiquan Liu;Yupeng Xiong;Yong Ma;Athanasios V. Vasilakos;Xinghua Li;Jianfeng Ma","doi":"10.1109/TMC.2025.3545140","DOIUrl":"https://doi.org/10.1109/TMC.2025.3545140","url":null,"abstract":"The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6643-6657"},"PeriodicalIF":7.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219696","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

Realistic Facial Expression Reconstruction Using Millimeter Wave 基于毫米波的逼真面部表情重建

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-25 DOI: 10.1109/TMC.2025.3540877

Hao Kong;Jiahong Xie;Jiadi Yu;Yingying Chen;Linghe Kong;Yanmin Zhu;Feilong Tang

{"title":"Realistic Facial Expression Reconstruction Using Millimeter Wave","authors":"Hao Kong;Jiahong Xie;Jiadi Yu;Yingying Chen;Linghe Kong;Yanmin Zhu;Feilong Tang","doi":"10.1109/TMC.2025.3540877","DOIUrl":"https://doi.org/10.1109/TMC.2025.3540877","url":null,"abstract":"The technology of facial expression reconstruction has paved the way for various face-centric applications such as virtual reality (VR) modeling, human-computer interaction, and affective computing. Existing vision-based solutions present challenges in privacy leakage and poor lighting conditions. In this paper, we introduce a nonintrusive facial expression reconstruction system, <italic>mm3DFace, which uses a millimeter wave (mmWave) radar to reconstruct facial expressions in a privacy-preserving and passive manner. <italic>mm3DFace first captures and pre-processes mmWave signals reflected by a human face, and extracts intricate facial geometric features using a ConvNeXt model integrated with triple loss embedding. Subsequently, <italic>mm3DFace derives pose-invariant facial representations utilizing region-divided affine transformation, and further generates individual facial shapes with 68 facial landmarks. Then, dynamic facial expressions with 3D facial avatars are reconstructed to exhibit realistic facial expressions. Finally, <italic>mm3DFace enables micro-expression recognition with mmWave signals, which ensures the capability of describing tiny facial changes. Through extensive real-world experiments involving 15 participants, <italic>mm3DFace achieves a normalized mean error of 3.94%, a mean absolute error of 2.30 mm, and a 3D-mean absolute error of 4.10 mm in tracking 68 facial landmarks, which demonstrates the efficacy and practicality of <italic>mm3DFace in real-world 3D facial reconstruction scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5964-5980"},"PeriodicalIF":7.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255759","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

Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge Computing 面向车辆边缘计算任务卸载的车辆辅助服务缓存

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-25 DOI: 10.1109/TMC.2025.3545444

Hongbo Jiang;Jianghao Cai;Zhu Xiao;Kehua Yang;Hongyang Chen;Jiangchuan Liu

{"title":"Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge Computing","authors":"Hongbo Jiang;Jianghao Cai;Zhu Xiao;Kehua Yang;Hongyang Chen;Jiangchuan Liu","doi":"10.1109/TMC.2025.3545444","DOIUrl":"https://doi.org/10.1109/TMC.2025.3545444","url":null,"abstract":"The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, a <underline>Vehicle-<underline>assisted <underline>Collaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6688-6700"},"PeriodicalIF":7.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219596","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

Individualized Data Generation in Personalized Federated Learning 个性化联邦学习中的个性化数据生成

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-24 DOI: 10.1109/TMC.2025.3545244

Yunyun Cai;Wei Xi;Yuhao Shen;Cerui Sun;Shuai Wang;Wei Gong;Jizhong Zhao

{"title":"Individualized Data Generation in Personalized Federated Learning","authors":"Yunyun Cai;Wei Xi;Yuhao Shen;Cerui Sun;Shuai Wang;Wei Gong;Jizhong Zhao","doi":"10.1109/TMC.2025.3545244","DOIUrl":"https://doi.org/10.1109/TMC.2025.3545244","url":null,"abstract":"Most Personalized Federated Learning (PFL) algorithms merge the model parameters of each client with other (similar or generic) model parameters to optimize the personalized model (PM). However, the merged model parameters in these algorithms may fit low relevance data, thereby limiting the performance of PM. In this paper, we generate similar data for each client through the collaboration of a generic model (GM) on the server, rather than merging model parameters. To train a generator capable of generating data for all classes on the server without real data, we employ the GM as the discriminator in adversarial training with the generator. Additionally, we introduce a similarity assessment metric, which allows for the assessment of the similarity between local data and data from other classes. Nevertheless, the presence of non-IID data among clients can weaken the performance of the GM, consequently impacting the training of the generator and similarity assessment. To address this issue, we design a directive mechanism so that GM can be optimized during adversarial training without the need for additional training. The experimental results validate the superiority of our algorithm over state-of-the-art algorithms in terms of accuracy, loss, and convergence speed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6628-6642"},"PeriodicalIF":7.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219760","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

ReflexGest: Recognizing Hand Gestures Under VLC-Capable Lamps ReflexGest：在vlc功能灯下识别手势

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-24 DOI: 10.1109/TMC.2025.3545340

Ziwei Liu;Jifei Zhu;Jiaqi Yang;Yimao Sun;Yanbing Yang;Jun Luo

{"title":"ReflexGest: Recognizing Hand Gestures Under VLC-Capable Lamps","authors":"Ziwei Liu;Jifei Zhu;Jiaqi Yang;Yimao Sun;Yanbing Yang;Jun Luo","doi":"10.1109/TMC.2025.3545340","DOIUrl":"https://doi.org/10.1109/TMC.2025.3545340","url":null,"abstract":"As a main approach towards touch-free human-computer interaction, <italic>hand gesture recognition (HGR) has long been a research focus for both academia and industry. Meanwhile, <italic>visible light communication (VLC) has become increasingly popular with VLC-ready commercial products (e.g., Philips lamps) available on the market. These facts provoke us to ask: can we leverage a VLC-ready lamp to realize <italic>integrated sensing and communication (ISAC) by conducting both HGR and VLC simultaneously? To this end, we propose ReflexGest as our answer to this question. ReflexGest is implemented upon a table lamp for the sake of practicality; this VLC-ready lamp is equipped with a ring-shaped light-emitting diode (LED) array and a photodiode (PD, for light intensity sensing) originally aiming for up/down-link VLCs. Demanding hand gestures to be performed between the lamp and a table surface, ReflexGest exploits the variation of the reflection and their unique correlation with the corresponding hand gestures to achieve HGR. In particular, ReflexGest first handles the limited sensing ability of the PD by enhancing the LED lamp and thus diversifying the light emission patterns. Moreover, ReflexGest combats the reflection interference from varying table surfaces via an adversarial learning technique to distill only the features relevant to hand gestures. Our extensive evaluations demonstrate that ReflexGest is able to deliver accurate HGR under realistic VLC traffic.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6583-6594"},"PeriodicalIF":7.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219597","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

Topology-Compressed Data Delivery in Large-Scale Heterogeneous Satellite Networks: An Age-Driven Spatial-Temporal Graph Neural Network Approach 大规模异构卫星网络中的拓扑压缩数据传输：一种年龄驱动的时空图神经网络方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-24 DOI: 10.1109/TMC.2025.3544574

Ronghao Gao;Bo Zhang;Qinyu Zhang;Zhihua Yang

{"title":"Topology-Compressed Data Delivery in Large-Scale Heterogeneous Satellite Networks: An Age-Driven Spatial-Temporal Graph Neural Network Approach","authors":"Ronghao Gao;Bo Zhang;Qinyu Zhang;Zhihua Yang","doi":"10.1109/TMC.2025.3544574","DOIUrl":"https://doi.org/10.1109/TMC.2025.3544574","url":null,"abstract":"In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring to obtain global topological information with huge computational cost and signaling overhead. To address this issue, in this paper, we propose an Age-predicting Local Information Dependable Transmission (ALIDT) mechanism for the LSHSN by considering the impact of time-varying topology on the timeliness of data, in which a novel metric of data freshness called Forwarding-aware Age of Information (FAoI) is well-designed to evaluate the timeliness in data forwarding at node. In particular, we develop a satellite Coverage-based Local Information Sharing (CLIS)-assisted Spatial-Temporal Graph Neural Network (STGNN) to extract the topological features in both temporal and spatial dimensions and a Graph Matching Network (GMN)-based topology compression algorithm to improve computation efficiency. The simulation results indicate that the proposed mechanism performs better in improving the storage overhead, throughput and average FAoI compared with the conventional Open Shortest Path First (OSPF) routing algorithm with Time-Varying Graph (TVG) model, GNN-based Multipath Routing (GMR) algorithm, and Gated Recurrent Units (GRU) based metric prediction algorithm in hybrid satellite networks, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6673-6687"},"PeriodicalIF":7.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219692","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

Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic Networks 移动机会网络的前向合法匿名组配对洋葱路由

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-24 DOI: 10.1109/TMC.2025.3544674

Xiuzhen Zhu;Limei Lin;Yanze Huang;Xiaoding Wang;Sun-Yuan Hsieh;Jie Wu

{"title":"Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic Networks","authors":"Xiuzhen Zhu;Limei Lin;Yanze Huang;Xiaoding Wang;Sun-Yuan Hsieh;Jie Wu","doi":"10.1109/TMC.2025.3544674","DOIUrl":"https://doi.org/10.1109/TMC.2025.3544674","url":null,"abstract":"Mobile Opportunistic Networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of “groups” into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol’s security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6595-6612"},"PeriodicalIF":7.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219584","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 MEC Server Strategy in Blockchain Networks: Mining for Mobile Users or for Self 探索区块链网络中的MEC服务器策略：为移动用户挖掘还是为自己挖掘

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-20 DOI: 10.1109/TMC.2025.3544311

Xintong Ling;Rui Jiang;Weihang Cao;Mingkai Chen;Jiaheng Wang;Zhi Ding;Xiqi Gao

{"title":"Exploring MEC Server Strategy in Blockchain Networks: Mining for Mobile Users or for Self","authors":"Xintong Ling;Rui Jiang;Weihang Cao;Mingkai Chen;Jiaheng Wang;Zhi Ding;Xiqi Gao","doi":"10.1109/TMC.2025.3544311","DOIUrl":"https://doi.org/10.1109/TMC.2025.3544311","url":null,"abstract":"Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features; nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6030-6044"},"PeriodicalIF":7.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243837","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

Build Yourself Before Collaboration: Vertical Federated Learning With Limited Aligned Samples 在协作之前先建立自己：有限对齐样本的垂直联邦学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-20 DOI: 10.1109/TMC.2025.3543923

Wei Shen;Mang Ye;Wei Yu;Pong C. Yuen

{"title":"Build Yourself Before Collaboration: Vertical Federated Learning With Limited Aligned Samples","authors":"Wei Shen;Mang Ye;Wei Yu;Pong C. Yuen","doi":"10.1109/TMC.2025.3543923","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543923","url":null,"abstract":"Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning paradigm that involves training models using distributed features from shared samples. However, the performance of VFL can be hindered when the number of shared or aligned samples is limited, a common issue in mobile environments where user data are diverse and unaligned across multiple devices. Existing approaches use feature generation and pseudo-label estimation for unaligned samples to address this issue, unavoidably introducing noise during the generation process. In this work, we propose Local Enhanced Effective Vertical Federated Learning (LEEF-VFL), which fully utilizes unaligned samples in the local learning before collaboration. Unlike previous methods that overlook private labels owned by each client, we leverage these private labels to learn from all local samples, constructing robust local models to serve as solid foundations for collaborative learning. Additionally, we reveal that the limited number of aligned samples introduces distribution bias from global data distribution. In this case, we propose to minimize the distribution discrepancies between the aligned samples and the global data distribution to enhance collaboration. Extensive experiments demonstrate the effectiveness of LEEF-VFL in addressing the challenges of limited aligned samples, making it suitable for VFL in mobile computing environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6503-6516"},"PeriodicalIF":7.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272763","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

MIDDLE: A Mobility-Driven Device-Edge-Cloud Federated Learning Framework 中间：移动驱动的设备-边缘云联合学习框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-19 DOI: 10.1109/TMC.2025.3543723

Songli Zhang;Zhenzhe Zheng;Fan Wu;Bingshuai Li;Yunfeng Shao;Guihai Chen

{"title":"MIDDLE: A Mobility-Driven Device-Edge-Cloud Federated Learning Framework","authors":"Songli Zhang;Zhenzhe Zheng;Fan Wu;Bingshuai Li;Yunfeng Shao;Guihai Chen","doi":"10.1109/TMC.2025.3543723","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543723","url":null,"abstract":"Federated learning (FL) can be implemented in large-scale wireless networks in a hierarchical way, introducing edge servers as relays between the cloud server and devices. These devices are dispersed within multiple clusters coordinated by edges. However, the devices are typically mobile users with unpredictable trajectories, and the impact of their mobility on the model training process is not well-studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE. MIDDLE addresses unbalanced model updates by capitalizing on model aggregation opportunities on mobile devices due to their mobility across edges. It consists of two components: on-device model aggregation, which aggregates models from different edges carried by mobile devices as they move across edges, and in-edge device selection, adjusting the current edge optimization direction through careful device selection. Theoretical analysis emphasizes that on-device model aggregation can reduce bias in model updating on edges and the cloud, thereby accelerating the FL model convergence. Building on this analysis, we introduce on-device global control averaging, modifying the training process on mobile devices and extending MIDDLE into <inline-formula><tex-math>$text{MIDDLE}^{+}$</tex-math></inline-formula>. Extensive experimental results validate that MIDDLE and <inline-formula><tex-math>$text{MIDDLE}^{+}$</tex-math></inline-formula> can reduce the time steps to reach the target accuracy by 19.44% and 20.37% at least, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4589-4606"},"PeriodicalIF":7.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925262","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