IEEE Transactions on Mobile Computing最新文献_第6页

SWIPTNet: A Unified Deep Learning Framework for SWIPT Based on GNN and Transfer Learning SWIPTNet：基于GNN和迁移学习的SWIPTNet统一深度学习框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-23 DOI: 10.1109/TMC.2025.3563892

Hong Han;Yang Lu;Zihan Song;Ruichen Zhang;Wei Chen;Bo Ai;Dusit Niyato;Dong In Kim

{"title":"SWIPTNet: A Unified Deep Learning Framework for SWIPT Based on GNN and Transfer Learning","authors":"Hong Han;Yang Lu;Zihan Song;Ruichen Zhang;Wei Chen;Bo Ai;Dusit Niyato;Dong In Kim","doi":"10.1109/TMC.2025.3563892","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563892","url":null,"abstract":"This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sum-rate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving near-optimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9477-9488"},"PeriodicalIF":9.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036850","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

Proactive Transport With High Link Utilization Using Opportunistic Packets in Cloud Data Centers 在云数据中心中利用机会数据包实现高链路利用率的主动传输

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-22 DOI: 10.1109/TMC.2025.3563182

Jinbin Hu;Jiawei Huang;Zhaoyi Li;Yijun Li;Shuying Rao;Wenchao Jiang;Kai Chen;Jianxin Wang;Tian He

{"title":"Proactive Transport With High Link Utilization Using Opportunistic Packets in Cloud Data Centers","authors":"Jinbin Hu;Jiawei Huang;Zhaoyi Li;Yijun Li;Shuying Rao;Wenchao Jiang;Kai Chen;Jianxin Wang;Tian He","doi":"10.1109/TMC.2025.3563182","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563182","url":null,"abstract":"To meet the stringent demanding low latency and high throughput of cloud datacenter applications, recent receiver-driven transport protocols transmit only one packet once receiving each credit packet from the receiver to achieve ultra-low queueing delay. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This article designs a simple yet effective solution called RPO, which retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. Furthermore, to tackle the queueing buildup due to line-rate transmission in the first RTT, we design a selective dropping mechanism called SDM to help the majority of small flows complete within only one RTT by prioritizing the first-RTT bursty packets over the packets triggered by grants. We implement RPO in Linux hosts with DPDK. The experimental results show that RPO significantly improves the network utilization by up to 35% over the state-of-the-art schemes, without introducing additional queueing delay. Moreover, RPO integrated with SDM reduces the AFCT of small flows by up to 45% compared with RPO integrated with Aeolus.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9774-9790"},"PeriodicalIF":9.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021153","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 Unlearning With Fast Recovery 联合学习与快速恢复

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-22 DOI: 10.1109/TMC.2025.3563265

Changjun Zhou;Chenglin Pan;Minglu Li;Pengfei Wang

{"title":"Federated Unlearning With Fast Recovery","authors":"Changjun Zhou;Chenglin Pan;Minglu Li;Pengfei Wang","doi":"10.1109/TMC.2025.3563265","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563265","url":null,"abstract":"Recent federated unlearning studies mainly focus on removing the target client's contributions from the global model permanently. However, the requirement for accommodating temporary user exits or additions in federated learning has been neglected. In this paper, we propose a novel recoverable federated unlearning scheme, named RFUL, which allows users to remove or add their local model to the global one at any time easily and quickly. It mainly consists of two main components, i.e., knowledge unlearning and knowledge recovery. In knowledge unlearning, the target contributions can be eliminated by training with mislabeled target data, while preserving the non-target contributions through distillation using the original model. In knowledge recovery, the forgotten contributions can be restored by training the target data using classification loss, while the non-target contributions are maintained through feature distillation and parameter freezing on the classifier. Both knowledge unlearning and recovery processes only require the participation of target data, guaranteeing the algorithm's practicality in federated learning systems. Extensive experiments demonstrate the significant efficacy of RFUL. For knowledge unlearning, RFUL matches state-of-the-art methods using only target data, achieving a runtime speedup of 3.3 to 8.7 times compared to retraining across various datasets. For knowledge recovery, RFUL exceeds state-of-the-art incremental learning methods by 5.02% to 29.97% in accuracy and achieves a runtime speedup of 1.8 to 4.4 times compared to retraining on different datasets.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9709-9725"},"PeriodicalIF":9.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021268","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

FedACS: An Adaptive Client Selection Framework for Communication-Efficient Federated Graph Learning 联邦图学习的自适应客户端选择框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-22 DOI: 10.1109/TMC.2025.3563404

Hongli Xu;Xianjun Gao;Jianchun Liu;Qianpiao Ma;Liusheng Huang

{"title":"FedACS: An Adaptive Client Selection Framework for Communication-Efficient Federated Graph Learning","authors":"Hongli Xu;Xianjun Gao;Jianchun Liu;Qianpiao Ma;Liusheng Huang","doi":"10.1109/TMC.2025.3563404","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563404","url":null,"abstract":"Federated graph learning (FGL) has been proposed to collaboratively train the increasing graph data with graph neural networks (GNNs) in a recommendation system. Nevertheless, implementing an efficient recommendation system with FGL still faces two primary challenges, i.e., limited communication bandwidth and non-IID local graph data. Existing works typically reduce communication frequency or transmission amount, which may suffer significant performance degradation under non-IID settings. Furthermore, some researchers propose to share the underlying structure among clients, which brings massive communication cost. To this end, we propose an efficient FGL framework, named FedACS, which adaptively selects a subset of clients for model training, to alleviate communication overhead and non-IID issues simultaneously. In FedACS, the global GNN model learns significant hidden edges and the structure of graph data among selected clients, enhancing recommendation efficiency. This capability distinguishes it from the traditional FL client selection methods. To optimize the client selection process, we introduce a multi-armed bandit (MAB) based algorithm to select participating clients according to the resource budgets and the training performance (i.e., RMSE). Experimental results indicate that FedACS improves RMSE by 5.4% over baselines with the same resource budget and reduces communication costs by up to 70.7% to achieve the same RMSE performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9760-9773"},"PeriodicalIF":9.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021428","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

SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading SPViT：通过自适应分割和卸载加速移动设备上的视觉转换推理

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562721

Sifan Zhao;Tongtong Liu;Hai Jin;Dezhong Yao

{"title":"SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading","authors":"Sifan Zhao;Tongtong Liu;Hai Jin;Dezhong Yao","doi":"10.1109/TMC.2025.3562721","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562721","url":null,"abstract":"The <italic>Vision Transformer</i> (ViT), which benefits from utilizing self-attention mechanisms, has demonstrated superior accuracy compared to CNNs. However, due to the expensive computational costs, deploying and inferring ViTs on resource-constrained mobile devices has become a challenge. To resolve this challenge, we conducted an empirical analysis to identify performance bottlenecks in deploying ViTs on mobile devices and explored viable solutions. In this paper, we propose SPViT, an adaptive split and offloading method that accelerates ViT inference on mobile devices. SPViT executes collaborative inference of ViT across available edge devices. We introduce a fine-grained splitting technique for the vision transformer structure. Furthermore, we propose an algorithm based on the Auto Regression model to predict partition latency and adaptive offload partitions. Finally, we design offline and online optimization methods to minimize the computational and communication overhead on each device. Based on real-world prototype experiments, SPViT effectively reduces inference latency by 2.2x to 3.3x across four state-of-the-art models.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9303-9318"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036223","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

IvyCross: A Privacy-Preserving and Concurrency Control Framework for Blockchain Interoperability 面向区块链互操作性的隐私保护和并发控制框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562875

Ming Li;Jian Weng;Jiasi Weng;Yi Li;Yongdong Wu;Dingcheng Li;Guowen Xu;Robert H. Deng

{"title":"IvyCross: A Privacy-Preserving and Concurrency Control Framework for Blockchain Interoperability","authors":"Ming Li;Jian Weng;Jiasi Weng;Yi Li;Yongdong Wu;Dingcheng Li;Guowen Xu;Robert H. Deng","doi":"10.1109/TMC.2025.3562875","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562875","url":null,"abstract":"Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design an incentive scheme based on smart contracts to stimulate the honest behavior of two hosts, bypassing the requirement of the number of TEE and large memory need. We examine the conditions to guarantee the uniqueness of Nash Equilibrium via Game Theory. Furthermore, an extended optimistic concurrency control protocol is designed to ensure the correctness of concurrent contracts execution. We formally prove the security of IvyCross in the Universal Composability (UC) framework and implement a prototype atop Bitcoin, Ethereum, and FISCO BOCS. Extensive experimental results on end-to-end performance and concurrency control demonstrate the efficiency and practicality of IvyCross.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9334-9351"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036741","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 Optimization of Beamforming and Trajectory for UAV-RIS-Assisted MU-MISO Systems Using GNN and SD3 基于GNN和SD3的无人机- ris辅助MU-MISO系统波束形成和轨迹联合优化

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3563072

Shumo Wang;Xiaoqin Song;Tiecheng Song;Yang Yang

{"title":"Joint Optimization of Beamforming and Trajectory for UAV-RIS-Assisted MU-MISO Systems Using GNN and SD3","authors":"Shumo Wang;Xiaoqin Song;Tiecheng Song;Yang Yang","doi":"10.1109/TMC.2025.3563072","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563072","url":null,"abstract":"In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate uncrewed aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV’s energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9539-9553"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036794","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 Positioning and Computation Offloading in Multi-UAV MEC for Low Latency Applications: A Proximal Policy Optimization Approach 面向低延迟应用的多无人机MEC联合定位与计算卸载：一种近端策略优化方法

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562806

Yuhui Wang;Junaid Farooq;Hakim Ghazzai;Gianluca Setti

{"title":"Joint Positioning and Computation Offloading in Multi-UAV MEC for Low Latency Applications: A Proximal Policy Optimization Approach","authors":"Yuhui Wang;Junaid Farooq;Hakim Ghazzai;Gianluca Setti","doi":"10.1109/TMC.2025.3562806","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562806","url":null,"abstract":"Multi-access edge computing (MEC) has emerged as a proven solution for reducing communication latency and enhancing user experience in delay-sensitive applications by offloading computation-intensive tasks to edge servers. In future networks, uncrewed aerial vehicles (UAVs), with their flexible deployment and reliable communication capabilities, have the potential to be deployed as aerial MEC servers in areas lacking cellular infrastructure. However, the joint optimization of UAV placement and task offloading poses significant challenges due to the interdependence between communication latency, computational demands, and the resource limitations of UAVs. In this paper, we propose a novel joint optimization framework utilizing proximal policy optimization (PPO) to simultaneously address UAV placement and computation offloading in UAV-enabled MEC networks. The framework dynamically adapts to changing network conditions, minimizing end-to-end latency while balancing computational loads and energy consumption. Extensive simulations demonstrate that the proposed PPO-based approach achieves superior performance compared to conventional optimization methods, with significant improvements in system latency, resource utilization, and network resilience. This work contributes scalable, adaptive solutions for UAV-assisted MEC networks in dynamic environments, enabling robust support for mission-critical and latency-sensitive applications.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9584-9598"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036730","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

REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction Over Sparse Trajectories 基于稀疏轨迹位置预测的人类移动性时变时间规律建模

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562669

Bangchao Deng;Bingqing Qu;Pengyang Wang;Dingqi Yang;Benjamin Fankhauser;Philippe Cudre-Mauroux

{"title":"REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction Over Sparse Trajectories","authors":"Bangchao Deng;Bingqing Qu;Pengyang Wang;Dingqi Yang;Benjamin Fankhauser;Philippe Cudre-Mauroux","doi":"10.1109/TMC.2025.3562669","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562669","url":null,"abstract":"Next-location prediction aims to forecast which location a user is most likely to visit given the user’s historical data. As a sequence modeling problem by nature, it has been widely addressed using Recurrent Neural Networks (RNNs). To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful. Existing solutions mostly incorporate spatiotemporal distances between locations in mobility traces, either by integrating them into the RNN units as additional information, or utilizing them to search for informative historical hidden states to improve prediction. However, such distance-based methods fail to capture the time-varying temporal regularities of human mobility, where human mobility is often more regular in the morning than in other time periods, for example; this suggests the usefulness of the actual timestamps besides the temporal distances. Under this circumstance, we propose REPLAY, learning to capture the time-varying temporal regularities for location prediction based on general RNN architecture. Specifically, REPLAY is designed on top of a flashback mechanism, where the spatiotemporal distances in sparse trajectories are used to search for the informative past hidden states; to accommodate the time-varying temporal regularities, REPLAY incorporates smoothed timestamp embeddings using Gaussian weighted averaging with timestamp-specific learnable bandwidths, which can flexibly adapt to the temporal regularities of different strengths across different timestamps. We conduct a comprehensive evaluation, comparing REPLAY against a wide range of state-of-the-art methods. Experimental results show REPLAY significantly and consistently outperforms state-of-the-art methods by 7.7%–10.5% in the location prediction task, and the learnt bandwidths reveal interesting patterns of the time-varying temporal regularities.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9428-9440"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036840","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

Tackling Resource Allocation for Decentralized Federated Learning: A GNN-Based Approach 解决分散联邦学习的资源分配：一种基于gnn的方法

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562834

Chuiyang Meng;Ming Tang;Mehdi Setayesh;Vincent W.S. Wong

{"title":"Tackling Resource Allocation for Decentralized Federated Learning: A GNN-Based Approach","authors":"Chuiyang Meng;Ming Tang;Mehdi Setayesh;Vincent W.S. Wong","doi":"10.1109/TMC.2025.3562834","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562834","url":null,"abstract":"Decentralized federated learning (DFL) enables clients to train a neural network model in a device-to-device (D2D) manner without central coordination. In practical systems, DFL faces challenges due to dynamic topology changes, time-varying channel conditions, and limited computational capability of the clients. These factors can affect the learning performance and efficiency of DFL. To address the aforementioned challenges, in this paper, we propose a graph neural network (GNN)–based algorithm to minimize the total delay and energy consumption on training and improve the learning performance of DFL in D2D wireless networks. In our proposed GNN, a multi-head graph attention mechanism is used to capture different features of clients and wireless channels. We design a neighbor selection module which enables each client to select a subset of its neighbors for the participation of model aggregation. We develop a decoder that enables each client to determine its transmit power and computational resource. Experimental results show that our proposed algorithm achieves a lower total delay and energy consumption on training when compared with five baseline schemes. Furthermore, by properly selecting a subset of neighbors for each client, our proposed algorithm achieves similar testing accuracy to the full participation scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9554-9569"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036885","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