IEEE Transactions on Services Computing最新文献

{"title":"Dual-View Deep Learning Approach for Predictive Business Process Monitoring","authors":"Binbin Chen;Shuangyao Zhao;Qiang Zhang;Chunhua Tang;Leilei Lin","doi":"10.1109/TSC.2025.3562344","DOIUrl":"10.1109/TSC.2025.3562344","url":null,"abstract":"Predictive business process monitoring (PBPM) is particularly valuable in dynamic business environments, and it can help organisations mitigate risks and optimise resource allocation. An interesting task in PBPM is next activity prediction (NAP), which allows the prediction of future activities that will be executed at a certain time based on ongoing business processes. Existing methods typically only utilise the order information of traces when predicting the next activity, without fully leveraging the attribute information present in the logs. Given the usefulness of these for NAP, combining them can help neural networks gain a deeper understanding of the actual business process. In this study, we propose a dual-view deep learning approach to fully extract and fuse the aforementioned two aspects of information. First, we treated traces as sequential texts and extracted the trace order information based on a long short-term memory based self-attention network. Then, we treated traces as unstructured images and captured the implicit attribute fusion information among events using a 12-layer residual network. Finally, two parts of information were fused for NAP. Experiments on 12 real-life event logs prove that the proposed approach is superior to state-of-the-art approaches, exhibiting good performance in accuracy, macro-precision, macro-recall, macro-F1-score, and macro-Gmean.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1368-1380"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849718","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}

{"title":"An Efficient Replication-Based Aggregation Verification and Correctness Assurance Scheme for Federated Learning","authors":"Shihong Wu;Yuchuan Luo;Shaojing Fu;Yingwen Chen;Ming Xu","doi":"10.1109/TSC.2024.3520833","DOIUrl":"10.1109/TSC.2024.3520833","url":null,"abstract":"Federated learning(FL), enabling multiple clients collaboratively to train a model via a parameter server, is an effective approach to address the issue of data silos. However, due to the self-interest and laziness of servers, they may not correctly aggregate the global model parameters, which will cause the final model trained to deviate from the training goal. In the existing proposals, the cryptography-based verification scheme involves heavy computation overheads. On the other hand, the replication-based verification method, relying on a dual-server architecture, can ensure the correctness of aggregation and reduce computation overheads, but incur at least twice the communication cost as that of the task itself. To address these issues, we propose a novel replication-based aggregation scheme for FL, which enables efficient verification and stronger correctness assurance. The scheme employs a main-secondary server architecture, which allows the secondary servers to partakes in aggregation tasks at a predetermined probability, consequently mitigating the validation overhead. Moreover, we resort to the game theory and design a Learning Contract to impose penalties on dishonest servers, enforcing rational servers to correctly compute global model parameters. Under the use of Betrayal Contract to prevent collusion among servers, we further design a training game to efficiently verify global model parameters and ensure their correctness. Finally, we analyze the correctness of the proposed scheme and demonstrate that the computational overhead of our scheme is <inline-formula><tex-math>$frac{{n + 1}}{{2n}}$</tex-math></inline-formula> of the previous replication-based validation scheme, obtaining a significant reduction in communication cost, where <inline-formula><tex-math>$n$</tex-math></inline-formula> means the training rounds. Experimental results further validate our deduction.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"633-646"},"PeriodicalIF":5.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822823","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}

{"title":"Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction","authors":"Guobing Zou;Zhi Yan;Shengxiang Hu;Yanglan Gan;Bofeng Zhang;Yixin Chen","doi":"10.1109/TSC.2025.3559613","DOIUrl":"10.1109/TSC.2025.3559613","url":null,"abstract":"Current state-of-the-art QoS prediction methods face two main limitations. First, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Second, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework named <underline>P</u>rivacy-<underline>E</u>nhanced <underline>F</u>ederated Expanded <underline>G</u>raph <underline>L</u>earning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1641-1654"},"PeriodicalIF":5.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819605","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}

{"title":"Synthetic Privacy-Preserving Trajectories With Semantic-Aware Dummies for Location-Based Services","authors":"Haojun Huang;Hao Sun;Weimin Wu;Chen Wang;Wuwu Liu;Wang Miao;Geyong Min","doi":"10.1109/TSC.2025.3556642","DOIUrl":"10.1109/TSC.2025.3556642","url":null,"abstract":"Trajectory synthesis with a series of fake locations has been deemed as a promising obfuscation technology to preserve the individual privacy of users in Location-Based Services (LBSs). However, a number of previous approaches fail to take into consideration the geographic distance and motion direction of the real locations to synthesize trajectories. As a result, most of them always cannot represent the statistical characteristics of real trajectories in a privacy-preserving manner, and thus suffer from various attacks through data analysis. To tackle this issue, this paper presents SPSD, a novel privacy-preserving trajectory synthesis approach with a <inline-formula><tex-math>$k$</tex-math></inline-formula>-anonymous guarantee, through extracting the semantic, geographic and directional similarity of locations from the real trajectories to create plausible trajectories. SPSD first classifies all historical trajectory data into a series of sets for location identity, by introducing the visiting time and visiting duration, which can clearly represent the semantic information of locations. Then, <inline-formula><tex-math>$ 4k$</tex-math></inline-formula> locations and <inline-formula><tex-math>$ 2k$</tex-math></inline-formula> of <inline-formula><tex-math>$ 4k$</tex-math></inline-formula> ones have been selected from each set to act as the initial disguises of each corresponding real location, with quantitative semantic and geographic similarities, respectively. In order to find enough fake locations for each real location in less time, the candidate locations have been narrowed down to <inline-formula><tex-math>$k$</tex-math></inline-formula> in direction recovery through step-by-step screening, with the <inline-formula><tex-math>$k$</tex-math></inline-formula>-anonymous property. Experiment results built on the real-world trajectory datasets indicate that SPSD has outperformed the previous approaches in terms of semantic similarity, directional accuracy and security resistance to synthesize privacy-preserving trajectories at the tolerable time cost.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1811-1824"},"PeriodicalIF":5.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775438","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}

{"title":"A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum","authors":"Juan Luis Herrera;Alejandro Moya;Javier Berrocal;Juan Manuel Murillo;Elena Navarro","doi":"10.1109/TSC.2025.3556641","DOIUrl":"10.1109/TSC.2025.3556641","url":null,"abstract":"The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this article, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1185-1198"},"PeriodicalIF":5.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757799","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}

{"title":"Reliable Service Recommendation: A Multi-Modal Adversarial Method for Personalized Recommendation Under Uncertain Missing Modalities","authors":"Junyang Chen;Ruohan Yang;Jingcai Guo;Huan Wang;Kaishun Wu;Liangjie Zhang","doi":"10.1109/TSC.2025.3556640","DOIUrl":"10.1109/TSC.2025.3556640","url":null,"abstract":"Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called <inline-formula><tex-math>$MMAM$</tex-math></inline-formula>, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind <inline-formula><tex-math>$MMAM$</tex-math></inline-formula> is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, <inline-formula><tex-math>$MMAM$</tex-math></inline-formula> successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1724-1738"},"PeriodicalIF":5.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757942","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}

{"title":"CrossMeta: A Fast and Cheap Cross-Metaverse Interoperability Protocol","authors":"Taotao Li, Qinglin Yang, Huawei Huang, Xuanye Zhu, Zhu Sun, Yuan Liu, Zibin Zheng","doi":"10.1109/tsc.2025.3556613","DOIUrl":"https://doi.org/10.1109/tsc.2025.3556613","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"26 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744965","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}

{"title":"DRL-Based Joint Optimization of Wireless Charging and Computation Offloading for Multi-Access Edge Computing","authors":"Xinyuan Zhu;Fei Hao;Lianbo Ma;Changqing Luo;Geyong Min;Laurence T. Yang","doi":"10.1109/TSC.2025.3556614","DOIUrl":"10.1109/TSC.2025.3556614","url":null,"abstract":"Wireless-powered multi-access edge computing (WP-MEC), as a promising computing paradigm with the great potential for breaking through the power limitations of wireless devices, is facing the challenges of reliable task offloading and charging power allocation. Towards this end, we formulate a joint optimization problem of wireless charging and computation offloading in socially-aware D2D-assisted WP-MEC to maximize the utility, characterized by wireless devices’ residual energy and the strength of social relationship. To address this problem, we propose a deep reinforcement learning (DRL)-based approach with hybrid actor-critic networks including three actor networks and one critic network as well as with Proximal Policy Optimization (PPO) updating policy. Further, to prevent the policy collapse, we adopt the PPO-clip algorithm which limits the update steps to enhance the stability of algorithm. The experimental results show that the proposed algorithm can achieved superior convergence performance and, meanwhile, improves the average utility efficiently compared to other baseline approaches.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1352-1367"},"PeriodicalIF":5.5,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744966","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}

{"title":"Federated Learning With Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach","authors":"Xuhan Zuo;Minghao Wang;Tianqing Zhu;Lefeng Zhang;Shui Yu;Wanlei Zhou","doi":"10.1109/TSC.2025.3553709","DOIUrl":"10.1109/TSC.2025.3553709","url":null,"abstract":"With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. This article introduces an innovative framework that melds blockchain with federated learning, ensuring an immutable record of unlearning requests and actions. Our approach not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Key contributions include a certification mechanism for the unlearning process, enhancement of data security and privacy, and optimization of data management. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our approach, achieving 0% accuracy for unlearned classes while maintaining 77.74% and 42.65% overall model accuracy for MNIST and CIFAR-10, respectively. Our time complexity analysis shows that the blockchain integration introduces only 2 seconds of overhead per epoch, highlighting the practicality of our solution for IoT applications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1428-1444"},"PeriodicalIF":5.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702679","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}

{"title":"pFedCal: Lightweight Personalized Federated Learning With Adaptive Calibration Strategy","authors":"Dongshang Deng;Xuangou Wu;Tao Zhang;Chaocan Xiang;Wei Zhao;Minrui Xu;Jiawen Kang;Zhu Han;Dusit Niyato","doi":"10.1109/TSC.2025.3553707","DOIUrl":"10.1109/TSC.2025.3553707","url":null,"abstract":"Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an <italic>aggregation gap</i> caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose <italic>pFedCal</i>, a novel <underline>p</u>ersonalized <underline>fed</u>erated learning with lightweight adaptive <underline>cal</u>ibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1627-1640"},"PeriodicalIF":5.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702641","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}