Pervasive and Mobile Computing最新文献_第2页

Digital twin-enabled age of information-aware scheduling for Industrial IoT edge networks 工业物联网边缘网络的信息感知调度数字孪生时代

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-16 DOI: 10.1016/j.pmcj.2025.102083

Elif Bozkaya-Aras

{"title":"Digital twin-enabled age of information-aware scheduling for Industrial IoT edge networks","authors":"Elif Bozkaya-Aras","doi":"10.1016/j.pmcj.2025.102083","DOIUrl":"10.1016/j.pmcj.2025.102083","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102083"},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An optimized Multi Agent Reinforcement Learning solution for edge caching in the Internet of Vehicles 一种针对车联网边缘缓存的优化多智能体强化学习解决方案

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-13 DOI: 10.1016/j.pmcj.2025.102081

Mohamed Amine Ghamri, Badis Djamaa, Mohamed Akrem Benatia, Redouane Bellahmer

{"title":"An optimized Multi Agent Reinforcement Learning solution for edge caching in the Internet of Vehicles","authors":"Mohamed Amine Ghamri, Badis Djamaa, Mohamed Akrem Benatia, Redouane Bellahmer","doi":"10.1016/j.pmcj.2025.102081","DOIUrl":"10.1016/j.pmcj.2025.102081","url":null,"abstract":"<div><div>The Internet of Vehicles has evolved significantly with the integration of intelligent technologies, transforming vehicular networks by enhancing communication, resource management, and decision-making at the network’s edge. With the increasing complexity of vehicular environments and data demands, efficient caching mechanisms have become essential to ensure seamless service delivery and optimized resource usage. In this paper, we present LF-MARLEC, a Leader Follower Multi-Agent Reinforcement Learning solution for Edge Caching within the Internet of Vehicles. Our approach introduces a hierarchical distribution of action importance, enabling more effective decision-making at the network edge. Extensive experiments, conducted using widely adopted simulation tools such as SUMO and Veins, demonstrate that our approach substantially enhances caching performance and overall system efficiency. Specifically, our approach achieves nearly 9% reduction in content distribution delay and over 11% improvement in cache hit rate compared to state-of-the-art methods, thereby enhancing the effectiveness of intelligent edge caching in Internet of Vehicles environments. The source code is publicly available at: <span><span>https://github.com/amine9008/RL-EDGE-CACHING</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102081"},"PeriodicalIF":3.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Lightweight secure key establishment to create a secure channel between entities in a crowdsourcing environment 轻量级安全密钥建立，在众包环境中创建实体之间的安全通道

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-09 DOI: 10.1016/j.pmcj.2025.102078

Mahdi Nikooghadam, Hamid Reza Shahriari

{"title":"Lightweight secure key establishment to create a secure channel between entities in a crowdsourcing environment","authors":"Mahdi Nikooghadam, Hamid Reza Shahriari","doi":"10.1016/j.pmcj.2025.102078","DOIUrl":"10.1016/j.pmcj.2025.102078","url":null,"abstract":"<div><div>The concept of crowdsourcing uses shared intelligence to solve complex tasks through group collaboration. Crowdsourcing involves gathering information and opinions from participants who submit their data, or solutions, over the Internet using a specific program. Given that the communication environment for crowdsourcing platforms is the Internet, there is a significant opportunity for attackers to compromise the confidentiality and integrity of information and violate participants’ privacy. Despite the great benefits of crowdsourcing, concerns about security and privacy are growing and require attention. Unfortunately based on our knowledge, the schemes presented to preserve security and privacy in crowdsourcing are susceptible to security and privacy attack and have a high computational and communication overhead. Therefore, they are not appropriate for crowdsourcing environments. This paper presents an ultra-lightweight authentication and key establishment protocol based on hash functions. This protocol meets all security requirements, is invulnerable to known attacks, and imposes a very low network overhead. The security of the proposed scheme has been formally proved, depicting the resistance of the proposed scheme to different types of possible attacks. In addition, the robustness of the proposed scheme against potential attacks has been proven through Scyther’s automatic software validation tool. The performance evaluation ultimately demonstrated that the proposed protocol incurs significantly reduced computational and communication costs compared to previous schemes and is very suitable for the crowdsourcing environment.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102078"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Unveiling user dynamics in the evolving social debate on climate crisis during the conferences of the parties 在缔约方会议期间，在不断发展的气候危机社会辩论中揭示用户动态

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-05 DOI: 10.1016/j.pmcj.2025.102077

Liliana Martirano , Lucio La Cava , Andrea Tagarelli

{"title":"Unveiling user dynamics in the evolving social debate on climate crisis during the conferences of the parties","authors":"Liliana Martirano , Lucio La Cava , Andrea Tagarelli","doi":"10.1016/j.pmcj.2025.102077","DOIUrl":"10.1016/j.pmcj.2025.102077","url":null,"abstract":"<div><div>Social media have widely been recognized as a valuable proxy for investigating users’ opinions by echoing virtual venues where individuals engage in daily discussions on a wide range of topics. Among them, climate change is gaining momentum due to its large-scale impact, tangible consequences for society, and enduring nature. In this work, we investigate the social debate surrounding climate emergency, aiming to uncover the fundamental patterns that underlie the climate debate, thus providing valuable support for strategic and operational decision-making. To this purpose, we leverage Graph Mining and NLP techniques to analyze a large corpus of tweets spanning seven years pertaining to the Conference of the Parties (COP), the leading global forum for multilateral discussion on climate-related matters, based on our proposed framework, named NATMAC, which consists of three main modules designed to perform network analysis, topic modeling and affective computing tasks. Our contribution in this work is manifold: (i) we provide insights into the key social actors involved in the climate debate and their relationships, (ii) we unveil the main topics discussed during COPs within the social landscape, (iii) we assess the evolution of users’ sentiment and emotions across time, and (iv) we identify users’ communities based on multiple dimensions. Furthermore, our proposed approach exhibits the potential to scale up to other emergency issues, highlighting its versatility and potential for broader use in analyzing and understanding the increasingly debated emergent phenomena.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102077"},"PeriodicalIF":3.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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-BEE-C: Autonomous Bandwidth-Efficient Edge Codecast A-BEE-C：自主带宽高效边缘编解码器

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-04 DOI: 10.1016/j.pmcj.2025.102075

Gyujeong Lim , Joon-Min Gil , Heonchang Yu

{"title":"A-BEE-C: Autonomous Bandwidth-Efficient Edge Codecast","authors":"Gyujeong Lim , Joon-Min Gil , Heonchang Yu","doi":"10.1016/j.pmcj.2025.102075","DOIUrl":"10.1016/j.pmcj.2025.102075","url":null,"abstract":"<div><div>Edge computing is a new paradigm in cloud infrastructure that decentralizes computing and storage, bringing data and services closer to the users. This proximity allows users to access high quality or large sized data with lower latency. However, edge servers typically have fewer resources than cloud servers, necessitating efficient resource management. Emerging research focuses on increasing the cache hit rate of user requests to edge servers, which reduces response latency and improves efficiency. Nonetheless, if available bandwidth is not considered, it becomes challenging to maintain both speed and quality in edge environments. This paper proposes an Autonomous Bandwidth-Efficient Edge Codecast (A-BEE-C) method to enhance the effective bandwidth per device within an edge service area. Codecast, introduced in this paper, is a transmission method that encodes multiple files into a single file before sending it to users. A-BEE-C introduces a dynamic mechanism that switches between unicast and codecast modes based on real-time bandwidth assessment. Our proposed method increases the effective bandwidth per device by encoding multiple user requests into a single coded transmission when the bandwidth of the edge server is limited. Experimental results demonstrate that A-BEE-C reduces average latency per device by up to 9.89% (and up to 18.45% with Zipf pattern data) and increases effective bandwidth per user by up to 10.15% (up to 18.11% with Zipf pattern).</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102075"},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Hybrid elk herd green anaconda-based multipath routing and deep learning-based intrusion detection In MANET 基于混合麋鹿群绿水蟒的多路径路由和基于深度学习的MANET入侵检测

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102079

Dr M. Anugraha , Dr S. Selvin Ebenezer , Dr S. Maheswari

{"title":"Hybrid elk herd green anaconda-based multipath routing and deep learning-based intrusion detection In MANET","authors":"Dr M. Anugraha , Dr S. Selvin Ebenezer , Dr S. Maheswari","doi":"10.1016/j.pmcj.2025.102079","DOIUrl":"10.1016/j.pmcj.2025.102079","url":null,"abstract":"<div><div>A Mobile Ad-Hoc Network (MANET) represents a set of wireless networks that create the network without requiring centralized control. Moreover, the MANET serves as an effectual communication network but is impacted by security issues. MANET intrusion detection constantly monitors network traffic for potential intrusions. Still, it requires network nodes for analyzing, and processing the data, which leads to the highest processing charge. For solving such difficulties, the EIK Herd Anaconda Optimization (EHAO)-based routing, and EHAO-trained Deep Kronecker Network (EHAO-DKN) for intrusion detection is devised in this paper. The MANET simulation is the prime step for attaining the routing. The proposed EHGAO with the fitness factors are considered in the routing. The intrusion presence in the MANET is detected at the Base Station (BS), where the Z-score normalization is applied to normalize the log data. The Wave Hedges metric effectively selects the relevant features, and the EHAO-DKN detects the intrusion. Furthermore, the EHAO-based routing obtained the optimal trust, energy, and delay of 85.30, 2.905 J, and 0.608 mS as well as the accuracy, sensitivity, and specificity of 92.40 %, 91.50 %, and 91.50 % are achieved by the EHAO-DKN-based intrusion detection.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102079"},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Differentiating presence in virtual reality using physiological signals 利用生理信号区分虚拟现实中的存在感

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102065

Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey

{"title":"Differentiating presence in virtual reality using physiological signals","authors":"Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey","doi":"10.1016/j.pmcj.2025.102065","DOIUrl":"10.1016/j.pmcj.2025.102065","url":null,"abstract":"<div><div>Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study (<em>N</em>=26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102065"},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Octopus: Knapsack model-driven federated learning client selection in internet of vehicles Octopus：车联网中背包模型驱动的联合学习客户端选择

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-16 DOI: 10.1016/j.pmcj.2025.102063

Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma

{"title":"Octopus: Knapsack model-driven federated learning client selection in internet of vehicles","authors":"Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma","doi":"10.1016/j.pmcj.2025.102063","DOIUrl":"10.1016/j.pmcj.2025.102063","url":null,"abstract":"<div><div>Federated learning (FL), as a distributed way for processing real-time vehicle data, is widely used to improve driving experience and enhance service quality in Internet of Vehicles (IoV). However, considering the data and devices heterogeneity of vehicle nodes, randomly selecting vehicles that are involved in model training would suffer from data skewness, high resource consumption, and low convergence speed. To this end, we propose <span>Octopus</span>, which consists of two components: i) an <em>importance sampling-based local loss computation</em> method is designed to request resource information for each client and apply the importance sampling technique to assess each client’s contribution to the global model’s convergence, followed by utilizing a knapsack model that treats the local loss of each client as the item value, while treating the total system training time as the knapsack capacity to accelerate the client convergence; ii) a <em>knapsack model-based federated learning client selection</em> method is designed to select the client with optimal local loss and maximum model uploading speed to participate in training. In each training round, these clients download and update the model within a predefined time, followed by enabling the selected clients to continue uploading the updated model parameters for assisting the server to efficiently complete the model aggregation. Experimental results show that <span>Octopus</span> improved the model accuracy by 2.64% <span><math><mo>∼</mo></math></span>32.61% with heterogeneous data, and by 1.97% <span><math><mo>∼</mo></math></span>11.74% with device heterogeneity, compared to eight state-of-the-art baselines.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102063"},"PeriodicalIF":3.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

EdgePlantNet: Lightweight edge-aware cyber–physical system for plant disease detection using enhanced attention CNNs EdgePlantNet：轻量级边缘感知网络物理系统，用于植物病害检测，使用增强的关注cnn

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-01 DOI: 10.1016/j.pmcj.2025.102059

Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey

{"title":"EdgePlantNet: Lightweight edge-aware cyber–physical system for plant disease detection using enhanced attention CNNs","authors":"Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey","doi":"10.1016/j.pmcj.2025.102059","DOIUrl":"10.1016/j.pmcj.2025.102059","url":null,"abstract":"<div><div>The advances in sensing and computing methodologies have allowed ubiquitous Cyber–Physical Systems (CPS) which have enabled intelligent monitoring and management of crop plants, leading to Smart Agriculture. Yet, the computational constraints of the edge-computing devices have been a roadblock for utilization of complex processing algorithms for real-time applications like leaf-disease detection, were immediate and highly accurate results are of paramount importance. To address this, we propose EdgePlantNet, a Lightweight Edge-Aware CPS for Plant Disease Detection using Enhanced Attention CNNs. It comprises a novel dual-branched Convolutional Neural Network (CNN) architecture that incorporates an improved multi-layered perceptron based spatial attention mechanism (MLP-ATCNN). The MLP-ATCNN is fed with both the original leaf image and its segmented copy, allowing it to simultaneously focus on the leaf image at two scales namely, the diseased regions, and the overall leaf. This allows it to learn robust discriminatory features corresponding to different diseases, even when trained with much lower samples of data. We validate the performance of the EdgePlantNet on two popular and diverse datasets that are the PlantVillage and the BPLD dataset. The novelty of our proposed CPS much lower computational complexity and high disease detection accuracy as compared to other state-of-the-art methods. We also implement the EdgePlantNet on a resource constraint IoT edge device, demonstrating its efficiency for mobile computing.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102059"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 learning with empirical insights: Leveraging gradient historical experiences for performance fairness 具有经验见解的联合学习：利用梯度历史经验来实现性能公平性

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-01 DOI: 10.1016/j.pmcj.2025.102061

Tongzhijun Zhu , Ying Lin , Yanzhen Qu , Zediao Liu , Yayu Luo , Tenglong Mao , Ziyi Chen

{"title":"Federated learning with empirical insights: Leveraging gradient historical experiences for performance fairness","authors":"Tongzhijun Zhu , Ying Lin , Yanzhen Qu , Zediao Liu , Yayu Luo , Tenglong Mao , Ziyi Chen","doi":"10.1016/j.pmcj.2025.102061","DOIUrl":"10.1016/j.pmcj.2025.102061","url":null,"abstract":"<div><div>Performance fairness has always been a key issue in federated learning (FL), however, the pursuit of performance consistency can lead to a trade-off where the accuracy of well-performing clients is compromised to enhance the accuracy of poor-performing clients. To ensure equitable treatment and unbiased outcomes for all participants in the FL process, we propose FedMH, a fair and fast multi-gradient descent federated learning algorithm with reinforced gradient historical empirical information. We have conducted a theoretical analysis of FedMH from the perspectives of fairness and convergence. Extensive experiments are performed on four federated datasets, revealing significant improvements achieved by FedMH compared to state-of-the-art baselines. Moreover, the experimental findings highlight FedMH’s superior performance in fine-grained classification problems when compared to existing advanced baselines. In brief, the proper utilization of gradient historical empirical information helps improve the effectiveness and fairness of FL, making it more suitable for large-scale and heterogeneous distributed environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102061"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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