IEEE Transactions on Emerging Topics in Computing最新文献_第8页

Concurrent Self-Testing and Uncertainty Estimation of Neural Networks Using Uncertainty Fingerprint 基于不确定指纹的神经网络并发自测试与不确定估计

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1109/TETC.2025.3640505

Soyed Tuhin Ahmed;Mehdi B. Tahoori

{"title":"Concurrent Self-Testing and Uncertainty Estimation of Neural Networks Using Uncertainty Fingerprint","authors":"Soyed Tuhin Ahmed;Mehdi B. Tahoori","doi":"10.1109/TETC.2025.3640505","DOIUrl":"https://doi.org/10.1109/TETC.2025.3640505","url":null,"abstract":"Neural networks (NNs) are increasingly used in always-on safety-critical applications deployed on hardware accelerators employing various memory technologies. Reliable, continuous operation of NN is essential for safety-critical applications. During online operation, NNs are susceptible to (single and multiple) permanent and soft errors due to factors such as radiation, aging, and thermal effects. Explicit testing methods for hardware accelerators cannot detect transient faults during inference, are unsuitable for always-on applications, and require extensive test vector generation and storage. Therefore, in this paper, we propose the <i>uncertainty fingerprint</i> approach that represents the online fault status of NN. Furthermore, we propose a dual-head NN topology specifically designed to produce uncertainty fingerprints and the primary prediction of the NN in <i>a single shot</i>. During the online operation, by matching the uncertainty fingerprint, we can concurrently self-test NNs with up to 100% coverage in the backbone and in the full model (with a modified approach) with a low false positive rate while maintaining the performance of the primary task similar to the baseline. Compared to existing works, memory overhead is reduced by up to 243.7 MB, multiply and accumulate (MAC) operations are reduced by up to <inline-formula><tex-math>$10000times$</tex-math></inline-formula>, and false-positive rates are reduced by up to 89%.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"118-133"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508129","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

MatcHEd: Privacy-Preserving Set Similarity Based on MinHash MatcHEd：基于MinHash的隐私保护集相似度

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1109/TETC.2025.3648749

Rostin Shokri;Charles Gouert;Nektarios Georgios Tsoutsos

{"title":"MatcHEd: Privacy-Preserving Set Similarity Based on MinHash","authors":"Rostin Shokri;Charles Gouert;Nektarios Georgios Tsoutsos","doi":"10.1109/TETC.2025.3648749","DOIUrl":"https://doi.org/10.1109/TETC.2025.3648749","url":null,"abstract":"Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, but certain applications remain prohibitively expensive in the encrypted domain. As a case in point, comparing two encrypted sets of data is extremely computationally expensive due to the large number of comparison operators required. In this work, we propose a novel methodology for encrypted set similarity inspired by the MinHash algorithm and the CGGI FHE scheme. Doing comparisons in FHE requires comparators and multiplexers or an expensive approximation, which further increases the latency, especially when the goal is to compare two sets of data. The MinHash algorithm can significantly reduce the number of comparisons required by employing a special Carter-Wegman (CW) hash function as a key building block. However, the modulus operation in the CW hash becomes another key bottleneck because the encrypted sub-circuits required to perform the modular reduction are very large and inefficient in an FHE setting. Towards that end, we introduce an efficient bitwise FHE-friendly digest function (FFD) to employ as the cornerstone of our proposed encrypted set-similarity. In a Boolean FHE scheme like CGGI, the bitwise operations can be implemented efficiently with Boolean gates, which allows for faster evaluation times relative to standard Carter-Wegman constructions. Overall, our approach drastically reduces the number of comparisons required relative to the baseline approach of directly computing the Jaccard similarity coefficients, and is inherently parallelizable, allowing for efficient encrypted computation on multi-CPU and GPU-based cloud servers. We validate our approach by performing a privacy-preserving plagiarism detection across encrypted documents.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"146-159"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509686","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

IsBad: Imperceptible Sample-Specific Backdoor to DNN With Denoising Autoencoder 用去噪自编码器对DNN进行难以察觉的样本特异性后门

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-11-17 DOI: 10.1109/TETC.2025.3631134

Xiangqi Wang;Mingfu Xue;Kewei Chen;Jing Xu;Wenmao Liu;Leo Yu Zhang;Yushu Zhang

{"title":"IsBad: Imperceptible Sample-Specific Backdoor to DNN With Denoising Autoencoder","authors":"Xiangqi Wang;Mingfu Xue;Kewei Chen;Jing Xu;Wenmao Liu;Leo Yu Zhang;Yushu Zhang","doi":"10.1109/TETC.2025.3631134","DOIUrl":"https://doi.org/10.1109/TETC.2025.3631134","url":null,"abstract":"The backdoor attack poses a new security threat to deep neural networks (DNNs). The existing backdoor often relies on visible universal triggers to make the backdoored model malfunction, which is not only usually visually suspicious to humans but also catchable by mainstream countermeasures. We propose an imperceptible sample-specific backdoor that the trigger varies from sample to sample and is invisible. Our trigger generation is automated through a denoising autoencoder that is fed with delicate but pervasive features (e.g., edge patterns per image). We extensively experiment with our backdoor attack on ImageNet and MS-Celeb-1M, which demonstrates a stable and nearly 100% (i.e., 99.8%) attack success rate with negligible impact on the clean data accuracy of the infected model. The denoising autoencoder-based trigger generator is reusable or transferable across tasks (e.g., from ImageNet to MS-Celeb-1M), whilst the trigger has high exclusiveness (i.e., a trigger generated for one sample is not applicable to another sample). Besides, our proposal backdoored model has achieved high evasiveness against mainstream backdoor defenses such as Neural Cleanse, STRIP, SentiNet, Fine-Pruning, BDMAE, and BTI-DBF(P).","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"30-41"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510397","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

Localization Graph Generation Based Matrix Factorization for Incomplete Multi-View Clustering 基于矩阵分解的不完全多视图聚类定位图生成

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1109/TETC.2026.3655898

Naiyao Liang;Zuyuan Yang;Dan Xiang;Jie Xu;Guoxu Zhou;Shengli Xie

{"title":"Localization Graph Generation Based Matrix Factorization for Incomplete Multi-View Clustering","authors":"Naiyao Liang;Zuyuan Yang;Dan Xiang;Jie Xu;Guoxu Zhou;Shengli Xie","doi":"10.1109/TETC.2026.3655898","DOIUrl":"https://doi.org/10.1109/TETC.2026.3655898","url":null,"abstract":"Incomplete multi-view clustering (IMC) has received increasing attention since missing observations of views are common in real-world applications. Existing approaches often learn similarity graphs from raw data, but the incompleteness of raw data limits their ability to capture complete similarity. Moreover, the local structure of the complete similarity graph is rarely considered. To alleviate these problems, in this paper, we propose a novel graph-based IMC method by designing a localization graph generation strategy on the weighted multi-view matrix factorization. Specifically, it first derives a consensus representation with balanced sample importance by the sample importance-weighted matrix factorization. Then, instead of learning graph from raw data, it develops a localization graph generation strategy to generate a complete consensus graph from the consensus representation. Especially, the dot-product weighting and the 0-1 weighting are learned adaptively, such that the local structure of the consensus graph is preserved well. Besides, the representation learning and localization graph generation are integrated into one joint optimization problem, where they boost each other. To optimize the proposed model, we provide an effective algorithm with a lemma and a theorem about its convergence. Finally, experimental results on several real-world datasets show the effectiveness and advancement of the proposed method, compared to the state-of-the-art methods.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"196-209"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511637","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

CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices 用于间歇性无电池设备的充电感知实时操作系统

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-12-19 DOI: 10.1109/TETC.2025.3643888

Mohsen Karimi;Yidi Wang;Youngbin Kim;Yoojin Lim;Hyoseung Kim

{"title":"CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices","authors":"Mohsen Karimi;Yidi Wang;Youngbin Kim;Yoojin Lim;Hyoseung Kim","doi":"10.1109/TETC.2025.3643888","DOIUrl":"https://doi.org/10.1109/TETC.2025.3643888","url":null,"abstract":"This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"54-68"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506340","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

Edge-Optimized Vision Transformers: A Co-Designed Hardware-Software Framework for Efficient Attention Mechanism 边缘优化视觉变压器：一种协同设计的高效注意力机制的软硬件框架

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1109/TETC.2026.3655830

Nisha Abdul Kareem;Naveen R;Sudhish N. George;Chithira P. R

{"title":"Edge-Optimized Vision Transformers: A Co-Designed Hardware-Software Framework for Efficient Attention Mechanism","authors":"Nisha Abdul Kareem;Naveen R;Sudhish N. George;Chithira P. R","doi":"10.1109/TETC.2026.3655830","DOIUrl":"https://doi.org/10.1109/TETC.2026.3655830","url":null,"abstract":"Vision Transformers (ViTs) effectively capture global context in videos/images using attention mechanisms, outperforming the traditional Convolutional Neural Networks (CNNs). However, the attention mechanism’s dependence on multiple matrix multiplications results in high computational complexity, posing a significant challenge for edge computing devices. This paper proposes a hardware-software co-design approach to mitigate this complexity. The software part utilizes a 50% sparsity pattern, which doubles throughput, and replaces the softmax function with linear Taylor attention for enhanced hardware efficiency. The hardware design introduces Recursive Array Matrix Multiplier (RAMM), a new architecture tailored for ViTs, which outperforms the conventional Systolic Array Matrix Multiplier (SAMM) used in similar works. RAMM is applicable to any deep neural network (DNN) architecture, but since ViTs are recent models, to demonstrate the versatility of RAMM, we integrate it into two ViT variants: the original ViT and MaxViT, a hybrid model that incorporates convolution. In addition, a Sparse Recursive Array Matrix Multiplier (SpaRMM), is also designed, which leverages 2:4 structured sparsity to achieve additional reductions in processing units and power consumption. The proposed architectures are synthesized using the AMS 90 nm library at a clock frequency of 500 MHz and also implemented on FPGA. The SpaRMM architecture achieves superior latency and throughput compared to RAMM, with increasing efficiency for higher-order matrices. Furthermore, RAMM demonstrates significant improvements over matrix multipliers in state-of-the-art co-design frameworks, such as SpAtten and <inline-formula><tex-math>$A^{3}$</tex-math></inline-formula>. To facilitate further research, our code is available at <uri>https://github.com/Nisha-Kareem/RAMM</uri>.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"184-195"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506807","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

Traceable and Revocable Attribute-Based Conjunctive Keyword Search 基于可追溯和可撤销属性的连接关键字搜索

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-02-03 DOI: 10.1109/TETC.2026.3658629

Jinguo Li;Chunlin Li;Kai Zhang;Yin He

引用次数: 0

Multi-Discounting Reinforcement Learning Based on Reward Decomposition 基于奖励分解的多重折扣强化学习

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-12-22 DOI: 10.1109/TETC.2025.3644748

Pengbin Chen;Qi Liu;Yanjie Li;Kejian Yan;Shuangkang Ma

{"title":"Multi-Discounting Reinforcement Learning Based on Reward Decomposition","authors":"Pengbin Chen;Qi Liu;Yanjie Li;Kejian Yan;Shuangkang Ma","doi":"10.1109/TETC.2025.3644748","DOIUrl":"https://doi.org/10.1109/TETC.2025.3644748","url":null,"abstract":"The standard paradigm of reinforcement learning (RL) is the Markov Decision Process (MDP) in which an agent learns to maximize the cumulative discounted rewards. The reward function in MDP is generally defined as the sum of multiple reward components, each designed to encapsulate a specific aspect of the expected policy. The discount factor <inline-formula><tex-math>$gamma in [0,1)$</tex-math></inline-formula> decreases the future reward in the present value, which determines the effective time horizon for the agent. In the conventional MDP, all reward components are subject to the same discount factor regardless of their specific meanings. Although this convenient configuration simplifies the problem in the algorithm deployment, it sacrifices precision in defining the optimization problem and results in a temporal mismatch of rewards with diverse physical meanings. This paper proposes multi-discounting MDP (MDMDP), a novel model based on reward decomposition to solve the above problems. MDMDP allows practitioners to set separate discount factors for different reward components. This capability provides great flexibility in combining reward components at different timescales. Furthermore, this paper proposes an RL algorithm, multi-discounting Q-learning, to solve finite MDMDP. Moreover, we extend it to the deep RL version, including multi-discounting DQN for discrete action space tasks and multi-discounting actor-critic for continuous action space tasks. Experimental results demonstrate that the proposed methods improve flexibility and precision in modeling complex tasks, enhancing the alignment of the agent’s policy with desired objectives.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"94-104"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510914","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

Runtime Termination Control in Stochastic Computing 随机计算中的运行时终止控制

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-12-22 DOI: 10.1109/TETC.2025.3644804

Owen S. Hoffend;John P. Hayes

{"title":"Runtime Termination Control in Stochastic Computing","authors":"Owen S. Hoffend;John P. Hayes","doi":"10.1109/TETC.2025.3644804","DOIUrl":"https://doi.org/10.1109/TETC.2025.3644804","url":null,"abstract":"Stochastic computing (SC) is an approximate digital design method that processes data in <inline-formula><tex-math>$N$</tex-math></inline-formula>-bit pseudo-random bitstreams. Unlike conventional binary computing (BC), SC has a valuable property called progressive precision (PP) where the bitstream length <inline-formula><tex-math>$N$</tex-math></inline-formula> can be varied continuously without changing the underlying hardware. Increasing <inline-formula><tex-math>$N$</tex-math></inline-formula> tends to reduce error at the cost of higher latency and energy usage. To exploit PP, typically a constant <inline-formula><tex-math>$N$</tex-math></inline-formula> is manually found such that the error does not exceed a target error <inline-formula><tex-math>${{epsilon }_{max}}$</tex-math></inline-formula> when averaged over all runs of the circuit. Noting that the optimal <inline-formula><tex-math>$N$</tex-math></inline-formula> varies depending on the circuit's specific input values, we address the following largely unexplored problem: Can we achieve better performance by employing a circuit that automatically calculates and adjusts <inline-formula><tex-math>$N$</tex-math></inline-formula> at runtime? To this end, we first present a general error model that defines the relationship between <inline-formula><tex-math>$N$</tex-math></inline-formula> and <inline-formula><tex-math>${{epsilon }_{max}}$</tex-math></inline-formula>. We then use this model to develop a latency-reducing method called PRET (precision-based runtime early termination). PRET finds and exploits unused bits of precision in the input while the SC circuit runs, identifying opportunities to reduce <inline-formula><tex-math>$N$</tex-math></inline-formula> without increasing the error. On a representative SC image processing application, we demonstrate that PRET reduces both latency and energy usage by about 3.5x with no accuracy loss and only a 1.63x increase in area, demonstrating that it provides a favorable trade-off for energy-constrained applications.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"69-79"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511278","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

Distributed Generalized Aggregative Equilibrium Seeking via Inexact Krasnosel’skii-Mann Iteration Over Time-Varying Communication Graphs 时变通信图上非精确Krasnosel - skii- mann迭代的分布式广义聚合均衡寻求

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-02-04 DOI: 10.1109/TETC.2026.3659349

Jun Li;Huaqing Li;Lifeng Zheng;Liang Ran;Lianghao Ji;Cuijuan Zhang

{"title":"Distributed Generalized Aggregative Equilibrium Seeking via Inexact Krasnosel’skii-Mann Iteration Over Time-Varying Communication Graphs","authors":"Jun Li;Huaqing Li;Lifeng Zheng;Liang Ran;Lianghao Ji;Cuijuan Zhang","doi":"10.1109/TETC.2026.3659349","DOIUrl":"https://doi.org/10.1109/TETC.2026.3659349","url":null,"abstract":"This paper investigates the generalized aggregative equilibrium (GAE) under globally-shared affine coupling constraints within the context of aggregative games involving a collective of agents. Specifically, the study is centered on a non-smooth composite game scenario, wherein each agent possesses a local Lipschitz- differentiable function coupled with a lower semi-continuous function. Utilizing the operator splitting technique, the dynamic tracking method and the successive approximation theorem, we progressively formulate three GAE seeking algorithms from requiring full decision information to partial decision information. These algorithms leverage the local, time-varying communication framework and the deployment of splitting operators characterized by maximal monotone, alleviate the stringent requirement for strongly monotone. By employing the inexact Krasnosel’skii-Mann (KM) iteration and the aggregation invariance, we derive strict bounds on the non-vanishing iterative steps, establishing convergence toward the variational GAE (v-GAE). Finally, the effectiveness and superiority of the proposed algorithms are demonstrated through their application to the time-varying networked Nash-Cournot game model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"254-271"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509840","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