IEEE Transactions on Mobile Computing最新文献

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

Feature-Based Machine Unlearning for Vertical Federated Learning in IoT Networks 物联网网络中垂直联邦学习的基于特征的机器学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-13 DOI: 10.1109/TMC.2025.3530529

Zijie Pan;Zuobin Ying;Yajie Wang;Chuan Zhang;Weiting Zhang;Wanlei Zhou;Liehuang Zhu

{"title":"Feature-Based Machine Unlearning for Vertical Federated Learning in IoT Networks","authors":"Zijie Pan;Zuobin Ying;Yajie Wang;Chuan Zhang;Weiting Zhang;Wanlei Zhou;Liehuang Zhu","doi":"10.1109/TMC.2025.3530529","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530529","url":null,"abstract":"In the era of the Internet of Things (IoT), managing the deluge of data generated by distributed devices presents unique challenges, particularly concerning privacy and the efficient use of computational resources. Vertical Federated Learning (VFL) offers a promising avenue for collaborative machine learning without centralizing data, thereby addressing privacy concerns inherent in traditional approaches. However, as data privacy laws and personal data deletion requests become more prevalent, the necessity for effective machine unlearning strategies within VFL frameworks grows increasingly important. To this end, this paper introduces a novel approach to feature-based machine unlearning tailored specifically for VFL systems in IoT networks. Our methodology enables the selective removal of data influence from trained models without the need for full retraining, thus preserving model utility while ensuring compliance with privacy requirements. By integrating a combination of feature relevance measuring techniques and efficient communication protocols, our solution minimizes the data footprint on network nodes, reduces bandwidth consumption, and maintains the integrity and performance of the learning models. To the best of our knowledge, our proposed framework represents the first practical approach to enable machine unlearning within vertical federated learning environments. We demonstrate the effectiveness of our approach through rigorous evaluation using several IoT datasets, highlighting significant improvements in unlearning efficiency and model robustness compared to existing techniques. Our work not only furthers the development of sustainable and compliant machine learning models in IoT but also sets a foundational framework for future research in secure and efficient data management within federated environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5031-5044"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918551","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

2024 Reviewers List 2024审稿人名单

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3527174

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

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

Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge 基于网络边缘自适应计算量的联邦共蒸馏加速

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-24 DOI: 10.1109/TMC.2025.3533591

Zhihao Zeng;Xiaoning Zhang;Yangming Zhao;Ahmed Zoha;Muhammad Ali Imran;Yan Zhang

{"title":"Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge","authors":"Zhihao Zeng;Xiaoning Zhang;Yangming Zhao;Ahmed Zoha;Muhammad Ali Imran;Yan Zhang","doi":"10.1109/TMC.2025.3533591","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533591","url":null,"abstract":"The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5584-5597"},"PeriodicalIF":7.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929818","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

A Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries 信息不对称下多访问边缘计算服务配置的讨价还价方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533045

Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein

{"title":"A Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries","authors":"Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein","doi":"10.1109/TMC.2025.3533045","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533045","url":null,"abstract":"Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve <inline-formula><tex-math>$text{79}{%}$</tex-math></inline-formula> of the optimal reduction in traffic given by a centralized optimal service placement strategy.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5464-5481"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929723","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

Minimization of the Training Makespan in Hybrid Federated Split Learning 混合联邦分割学习中训练最大时间跨度的最小化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533033

Joana Tirana;Dimitra Tsigkari;George Iosifidis;Dimitris Chatzopoulos

{"title":"Minimization of the Training Makespan in Hybrid Federated Split Learning","authors":"Joana Tirana;Dimitra Tsigkari;George Iosifidis;Dimitris Chatzopoulos","doi":"10.1109/TMC.2025.3533033","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533033","url":null,"abstract":"Parallel Split Learning (SL) allows resource-constrained devices that cannot participate in Federated Learning (FL) to train deep neural networks (NNs) by splitting the NN model into parts. In particular, such devices (clients) may offload the processing task of the largest model part to a computationally powerful helper, and multiple helpers may be employed and work in parallel. In hybrid federated and split learning (HFSL), on the other hand, devices can participate in the training process through any of the two protocols (SL and FL), depending on the system's characteristics. This could considerably reduce the maximum training time over all clients (makespan), especially in highly heterogeneous scenarios. In this paper, we study the joint problem of the training protocol selection, client-helper assignments, and scheduling decisions, to minimize the training makespan. We prove this problem is NP-hard and propose two solution methods: one based on the decomposition of the problem by leveraging its inherent symmetry, and a second fully scalable one. Through numerical evaluations using our testbed's measurements, we build a solution strategy comprising these methods. Moreover, this strategy finds a near-optimal solution and achieves a shorter makespan than the baseline schemes by up to 71%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5400-5417"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929817","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