IEEE Transactions on Emerging Topics in Computing最新文献

Breakout Local Search for Load-Balanced Federated Learning in Multi-BS Networks Multi-BS网络中负载均衡联邦学习的突破局部搜索

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-03-02 DOI: 10.1109/TETC.2026.3667101

Mina Kato;Xun Yuan;Tiago Koketsu Rodrigues;Fengxiao Tang;Ming Zhao;Nei Kato

{"title":"Breakout Local Search for Load-Balanced Federated Learning in Multi-BS Networks","authors":"Mina Kato;Xun Yuan;Tiago Koketsu Rodrigues;Fengxiao Tang;Ming Zhao;Nei Kato","doi":"10.1109/TETC.2026.3667101","DOIUrl":"https://doi.org/10.1109/TETC.2026.3667101","url":null,"abstract":"Federated learning (FL) relies on timely participation of multiple devices, yet the round time is often dominated by the slowest user under heterogeneous wireless conditions. In multi-base-station (multi-BS) environments with nonlinear concurrent uplink and diverse backhaul capacities, selecting an appropriate set of participants becomes a challenging combinatorial optimization problem. We cast the problem as a round-time–centric optimization with reference-latency-scaled fairness penalties to enforce diversity, cohort size, and BS load balance. To solve it efficiently, we propose a Breakout Local Search (BLS) solver that couples k-opt local refinement with approximate screening and adaptive three-mode perturbations under a wall-clock budget, enabling efficient exploration–exploitation. Extensive simulations under hotspot and throttled-backhaul scenarios show that the proposed method reduces round time by 80–90% compared with particle swarm optimization (PSO) and Random, and by 29–75% compared with Greedy and simulated annealing (SA), while lowering the overall objective by 80–84% relative to PSO and Random and 50–67% relative to Greedy and SA, across various diversity weights and target participant sizes. The results highlight the effectiveness of our BLS method in FL participant selection under realistic and difficult network conditions.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"364-376"},"PeriodicalIF":5.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147429294","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

Graph-Based Anomaly APT Attack Detection via Threat Intelligence 基于威胁情报的基于图的异常APT攻击检测

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-02-24 DOI: 10.1109/TETC.2026.3665235

Chun-I Fan;Cheng-Han Shie;Ying-Chan Chang;Tao Ban;Tomohiro Morikawa;Takeshi Takahashi

{"title":"Graph-Based Anomaly APT Attack Detection via Threat Intelligence","authors":"Chun-I Fan;Cheng-Han Shie;Ying-Chan Chang;Tao Ban;Tomohiro Morikawa;Takeshi Takahashi","doi":"10.1109/TETC.2026.3665235","DOIUrl":"https://doi.org/10.1109/TETC.2026.3665235","url":null,"abstract":"Among Advanced Persistent Threats in recent years, hackers have combined multiple defense evasion techniques to hide themselves from the detection of traditional antivirus software. For example, the combination of fileless malware and Living Off the Land techniques and abusing legitimate cloud services force the enterprises have gradually adopted the Endpoint Detection and Response (EDR) instead. However, EDR has the disadvantage that this tool may produce massive false alarms. This situation force security maintainer and analysts to be burdened with a large amount of additional analyses. We proposed an anomaly detection system based on graphs. First, we input a provenance graph containing threat intelligence constructed by the normal behaviors of the system. After that, the system learns the potential structured information from the provenance graph for detecting the abnormal behavior of a host. The results show that the proposed system can effectively detect abnormal event logs. Moreover, we reduce the number of false alarms by up to 97.67%. The improvement dramatically reduces the heavy burdens on the security maintainers from the analyses of the records. Furthermore, the performance of the designed system shows that the abnormal detection based on the graph neural network is superior to a traditional neural network.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"348-363"},"PeriodicalIF":5.4,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147429290","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

Anonymous Task Assignment and Worker Payment in Mobile Crowdsensing 移动众测中的匿名任务分配与工人报酬

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-02-16 DOI: 10.1109/TETC.2026.3662784

Tyler Nicewarner;Ali Allami;Dan Lin

{"title":"Anonymous Task Assignment and Worker Payment in Mobile Crowdsensing","authors":"Tyler Nicewarner;Ali Allami;Dan Lin","doi":"10.1109/TETC.2026.3662784","DOIUrl":"https://doi.org/10.1109/TETC.2026.3662784","url":null,"abstract":"Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge–response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system (Koutsos et al. 2025), which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP’s efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"332-347"},"PeriodicalIF":5.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147429297","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

FairRFL: Fair and Robust Federated Learning in the Presence of Selfish Clients FairRFL：自私客户端存在下的公平稳健的联邦学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-02-11 DOI: 10.1109/TETC.2026.3661199

Andrea Augello;Ashish Gupta;Giuseppe Lo Re;Sajal K. Das

{"title":"FairRFL: Fair and Robust Federated Learning in the Presence of Selfish Clients","authors":"Andrea Augello;Ashish Gupta;Giuseppe Lo Re;Sajal K. Das","doi":"10.1109/TETC.2026.3661199","DOIUrl":"https://doi.org/10.1109/TETC.2026.3661199","url":null,"abstract":"Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients “selfish”. This work proposes a <bold>Fair</b> and <bold>R</b>obust strategy for aggregation in the <bold>Federated Learning (FL)</b> server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel technique to recover (or estimate) the true updates from selfish clients by using robust statistics, specifically the median of norms. The presented strategy, through the inclusion of the recovered updates in the aggregation process, is robust against selfish behavior. Through extensive empirical evaluations with WISDM-W and CIFAR-10 datasets, we observe that a selfish client can increase the model accuracy on its data by up to 39% and more than quadruple the accuracy variance among clients, which FairRFL can address perfectly and recover performance fairness across normal clients.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"316-331"},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11391495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147429287","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

Toward Scalable Multi-Chip Wireless Networks With Near-Field Time Reversal 具有近场时间反转的可扩展多芯片无线网络

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-02-10 DOI: 10.1109/TETC.2026.3661404

Ama Bandara;Fátima Rodríguez-Galán;Pau Talarn;Elana Pereira de Santana;Evgenii Vinogradov;Peter Haring Bolívar;Eduard Alarcón;Sergi Abadal

{"title":"Toward Scalable Multi-Chip Wireless Networks With Near-Field Time Reversal","authors":"Ama Bandara;Fátima Rodríguez-Galán;Pau Talarn;Elana Pereira de Santana;Evgenii Vinogradov;Peter Haring Bolívar;Eduard Alarcón;Sergi Abadal","doi":"10.1109/TETC.2026.3661404","DOIUrl":"https://doi.org/10.1109/TETC.2026.3661404","url":null,"abstract":"The concept of Wireless Network-on-Chip (WNoC) has emerged as a potential solution to address the escalating communication demands of modern computing systems due to its low-latency, versatility, and reconfigurability. However, for WNoC to fulfill its potential, it is essential to establish multiple high-speed wireless links across chips. Unfortunately, the compact and enclosed nature of computing packages introduces significant challenges in the form of Co-Channel Interference (CCI) and Inter-Symbol Interference (ISI), which not only hinder the deployment of multiple spatial channels, but also severely restrict the symbol rate of each individual channel. In this paper, we posit that Time Reversal (TR) could be effective in addressing both impairments in this static scenario, thanks to its spatiotemporal focusing capabilities even in the near-field. Through comprehensive full-wave simulations and bit error rate analysis in multiple chip layouts with multiple frequency bands, we provide evidence that TR can increase the symbol rate by an order of magnitude, enabling the deployment of multiple concurrent links and achieving aggregate speeds exceeding 100 Gb/s. Finally, we evaluate the impact of reducing the sampling rate of the TR filter on the achievable speeds, paving the way to practical TR-based wireless communications at the chip scale.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"303-315"},"PeriodicalIF":5.4,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11391496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147429265","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

IEEE Transactions on Emerging Topics in Computing Publication Information IEEE计算出版信息新兴主题汇刊

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-12-05 DOI: 10.1109/TETC.2025.3633547

引用次数: 0

Multi-View Partial Multi-Label Learning via Class Activation Specific Features Collaborative Learning 基于类激活的多视图部分多标签学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-12 DOI: 10.1109/TETC.2025.3629677

Anhui Tan;Jianhang Xu;Weiping Ding;Jiye Liang;Witold Pedrycz

{"title":"Multi-View Partial Multi-Label Learning via Class Activation Specific Features Collaborative Learning","authors":"Anhui Tan;Jianhang Xu;Weiping Ding;Jiye Liang;Witold Pedrycz","doi":"10.1109/TETC.2025.3629677","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629677","url":null,"abstract":"Multi-view partial multi-label learning deals with scenarios where samples contain heterogeneous features and are associated with both relevant and corrupted labels. Existing methods struggle to effectively capture label-related features through adequate feature interaction while simultaneously integrating inter- and intra-view features. To address these challenges, we propose a robust and scalable framework, Class Activation Specific Features Collaborative Network, designed to handle feature heterogeneity and facilitate comprehensive feature fusion in multi-view partial multi-label learning. The framework integrates label-specific feature extraction with collaborative information propagation through two key components: 1) View-Specific Class Activation Map, which transforms multi-view features into compact class label representations and 2) Class Information Propagation Correction, which refines and propagates accurate class label information by leveraging graph convolutional networks and transformers. Additionally, we introduce a multi-faceted loss function that promotes robust feature learning and architectural stability via consistency-based structural loss, while improving generalization through knowledge distillation. Extensive experiments on benchmark datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods in multi-view partial multi-label learning tasks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1522-1535"},"PeriodicalIF":5.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729447","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

HIFLA: Hilbert-Inspired Federated Learning via Action Principles HIFLA：希尔伯特启发的基于行动原则的联邦学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-11 DOI: 10.1109/TETC.2025.3629528

Koffka Khan

{"title":"HIFLA: Hilbert-Inspired Federated Learning via Action Principles","authors":"Koffka Khan","doi":"10.1109/TETC.2025.3629528","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629528","url":null,"abstract":"Federated learning (FL) often suffers from client heterogeneity – differences in data distributions and learning behavior across clients that can degrade the global model’s performance. This paper addresses this challenge with HIFLA (Hilbert-Inspired Federated Learning via Action Principles), a novel approach that leverages variational mechanics. HIFLA formulates the federated training process as the minimization of a global action functional, yielding entropy- regularized Euler–Lagrange dynamics for client and server updates. A key innovation is the introduction of an <italic>interaction potential</i> among client models, which mitigates divergence caused by non-i.i.d. data by coupling their updates in the action formulation. Empirically, HIFLA improves model accuracy on heterogeneous FL benchmarks, outperforming standard methods in the presence of statistical heterogeneity. It also demonstrates enhanced robustness against adversarial clients: even when a fraction of participants behave maliciously or send corrupted updates, the HIFLA-based model converges reliably with minimal performance loss. Overall, our results indicate that an action-principle-driven paradigm can effectively tackle client heterogeneity and adversarial robustness in federated learning, paving the way for more resilient and generalizable FL systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1536-1552"},"PeriodicalIF":5.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729326","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 Novel Proportional-Integral-Parameter Zeroing Neural Network and Its Application to the Quaternion-Valued Time-Varying Linear Matrix Inequality 一种新型比例-积分-参数归零神经网络及其在四元数值时变线性矩阵不等式中的应用

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-11 DOI: 10.1109/TETC.2025.3629357

Jiajie Luo;Jiguang Li;Lin Xiao;Jichun Li;Wenxing Ji;William Holderbaum;Peng Qi

{"title":"A Novel Proportional-Integral-Parameter Zeroing Neural Network and Its Application to the Quaternion-Valued Time-Varying Linear Matrix Inequality","authors":"Jiajie Luo;Jiguang Li;Lin Xiao;Jichun Li;Wenxing Ji;William Holderbaum;Peng Qi","doi":"10.1109/TETC.2025.3629357","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629357","url":null,"abstract":"Zeroing neural network (ZNN) with variable convergence parameter has been a research hotspot recently, and it can be divided into two categories: the varying-parameter ZNN (VP-ZNN) model, and the fuzzy-parameter ZNN (FP-ZNN) model. Both of the two models have their own advantages and disadvantages. VP-ZNN models are usually efficient but not intelligent, while FP-ZNN models are usually intelligent but not efficient. Inspired by the classic proportional–integral–derivative (PID) control technology, we proposed a novel proportional-integral-parameter ZNN (PIP-ZNN) model, which is both efficient and intelligent, to solve the quaternion-valued time-varying linear matrix inequalities (QVTV-LMI) problem. Integrated with adaptive convergence parameters (ACP), the PIP-ZNN model dynamically adjusts its convergence in response to error changes and then achieves optimized performance. Compared with FP-ZNN models which are based on fuzzy logic systems, the PID-based PIP-ZNN models are simpler and more efficient. With the incorporation of a robust activation function (RAF), the PIP-ZNN model demonstrates fixed-time stability and robustness in the presence of both attenuated and constant disturbances. Theoretical analyses in this paper establish the fixed-time stability and robustness of the PIP-ZNN model, including an estimation of the upper bound of the settling-time function. Numerical experiments here validate these advanced features further, emphasizing the efficacy and excellent performance of the proposed PIP-ZNN model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1565-1576"},"PeriodicalIF":5.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729402","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

GraphMamba: Graph Tokenization Mamba for Hyperspectral Image Classification 用于高光谱图像分类的图标记化曼巴

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-05 DOI: 10.1109/TETC.2025.3626943

Muhammad Ahmad;Manuel Mazzara;Salvatore Distefano;Adil Mehmood Khan;Muhammad Hassaan Farooq Butt;Muhammad Usama;Danfeng Hong

{"title":"GraphMamba: Graph Tokenization Mamba for Hyperspectral Image Classification","authors":"Muhammad Ahmad;Manuel Mazzara;Salvatore Distefano;Adil Mehmood Khan;Muhammad Hassaan Farooq Butt;Muhammad Usama;Danfeng Hong","doi":"10.1109/TETC.2025.3626943","DOIUrl":"https://doi.org/10.1109/TETC.2025.3626943","url":null,"abstract":"Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as in HSI applications. To overcome such challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. This approach enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1510-1521"},"PeriodicalIF":5.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729314","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