Pub Date : 2026-01-21DOI: 10.1109/TNSE.2026.3656220
Xueting Yang;Zhong Li;Changjun Jiang
The anonymity of Bitcoin makes transaction tracing challenging, fostering illicit activities such as money laundering. Existing anti-money laundering (AML) approaches based on graph learning have improved detection performance by modeling structural properties of transaction networks, but they still rely on limited node features, which restrict their ability to capture complex laundering behaviors. To address this limitation, we propose a semantic-augmented bipartite graph learning framework for Bitcoin money laundering detection, which leverages large language models (LLMs) to enrich node semantics beyond structural information. Specifically, we model Bitcoin transaction networks as address–transaction bipartite graphs, and design a behavior-aware aggregation scheme to capture asymmetric interactions between heterogeneous nodes, enabling the extraction of rich structural information. To enrich node semantics, we depart from anomaly-centric paradigms and instead model normative transaction behavior as a statistical baseline. Deviations from this baseline are embedded into prompts to guide LLMs in generating natural-language descriptions of suspicious behaviors. Then, these LLM-based semantic representations are fused with graph embeddings through a multi-task learning framework with dynamic weighting, enabling the model to capture both interactional relationships and semantic cues. Experiments on two real-world Bitcoin datasets demonstrate that our approach achieves superior recall, F1-score, and AUC compared to state-of-the-art baselines, highlighting the effectiveness of semantic augmentation with LLMs in money laundering detection.
{"title":"Beyond Graph Structure: Semantic Augmentation With LLMs for Bitcoin Money Laundering Detection Under Economic Networks","authors":"Xueting Yang;Zhong Li;Changjun Jiang","doi":"10.1109/TNSE.2026.3656220","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3656220","url":null,"abstract":"The anonymity of Bitcoin makes transaction tracing challenging, fostering illicit activities such as money laundering. Existing anti-money laundering (AML) approaches based on graph learning have improved detection performance by modeling structural properties of transaction networks, but they still rely on limited node features, which restrict their ability to capture complex laundering behaviors. To address this limitation, we propose a semantic-augmented bipartite graph learning framework for Bitcoin money laundering detection, which leverages large language models (LLMs) to enrich node semantics beyond structural information. Specifically, we model Bitcoin transaction networks as address–transaction bipartite graphs, and design a behavior-aware aggregation scheme to capture asymmetric interactions between heterogeneous nodes, enabling the extraction of rich structural information. To enrich node semantics, we depart from anomaly-centric paradigms and instead model normative transaction behavior as a statistical baseline. Deviations from this baseline are embedded into prompts to guide LLMs in generating natural-language descriptions of suspicious behaviors. Then, these LLM-based semantic representations are fused with graph embeddings through a multi-task learning framework with dynamic weighting, enabling the model to capture both interactional relationships and semantic cues. Experiments on two real-world Bitcoin datasets demonstrate that our approach achieves superior recall, F1-score, and AUC compared to state-of-the-art baselines, highlighting the effectiveness of semantic augmentation with LLMs in money laundering detection.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6366-6382"},"PeriodicalIF":7.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175595","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}
Mobile Edge Computing (MEC) enables the delegation of computing tasks from Consumer Electronics (CEs) to edge servers. This offloading process significantly reduces the latency and energy consumption associated with CEs. Nonetheless, Deep Reinforcement Learning (DRL)-based offloading techniques often encounter challenges in reaching optimal solutions within a confined number of iterations due to the inherent complexity of the task. In light of this challenge, this paper introduces an approach that integrates DRL with Hyper-dimensional Networks (HDN) for task offloading, aiming to improve the efficiency of MEC systems. First, we establish a dynamic model of the MEC system and formulate the task-offloading problem to minimize the cumulative cost incurred by the MEC. Subsequently, we advance an offloading algorithm grounded in HDN principles. The experimental findings demonstrate that DRL with HDN leads to a marked reduction in the computational overhead of MEC systems when contrasted with alternative methodologies. Compared to the baseline algorithm, the proposed HDN-enhanced DRL reduces energy consumption, latency, and system consumption by 10.3%, 14.5%, and 10%, respectively.
{"title":"Hyper-Dimensional Computing Powered DRL for Task Offloading in Edge-Enabled Consumer Electronics","authors":"Xuejian Zhao;Xiaoming He;Xiaoming Xu;Hadeel Alsolai","doi":"10.1109/TNSE.2026.3656226","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3656226","url":null,"abstract":"Mobile Edge Computing (MEC) enables the delegation of computing tasks from Consumer Electronics (CEs) to edge servers. This offloading process significantly reduces the latency and energy consumption associated with CEs. Nonetheless, Deep Reinforcement Learning (DRL)-based offloading techniques often encounter challenges in reaching optimal solutions within a confined number of iterations due to the inherent complexity of the task. In light of this challenge, this paper introduces an approach that integrates DRL with Hyper-dimensional Networks (HDN) for task offloading, aiming to improve the efficiency of MEC systems. First, we establish a dynamic model of the MEC system and formulate the task-offloading problem to minimize the cumulative cost incurred by the MEC. Subsequently, we advance an offloading algorithm grounded in HDN principles. The experimental findings demonstrate that DRL with HDN leads to a marked reduction in the computational overhead of MEC systems when contrasted with alternative methodologies. Compared to the baseline algorithm, the proposed HDN-enhanced DRL reduces energy consumption, latency, and system consumption by 10.3%, 14.5%, and 10%, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6310-6324"},"PeriodicalIF":7.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175597","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}
The low-altitude economy has experienced significant growth recently, due to the flexibility, affordability, and versatility of low-altitude aircraft. A typical setup uses multiple devices in a distributed system, where the Byzantine Fault Tolerance (BFT) consensus ensures data consistency. However, existing BFT protocols, such as those based on the BKR paradigm with HoneybadgerBFT as a representative, suffer from high communication overhead and latency, limiting scalability and performance. In this paper, we introduce Vega, a novel BFT protocol designed to overcome these challenges. Vega replaces the traditional Reliable Broadcast (RBC) protocol with a more efficient linear Consistent Broadcast (CBC), reducing communication overhead from $O(n^{3})$ to $O(n^{2})$. However, this change introduces a new challenge related to totality, which may impact liveness. Additionally, Vega incorporates a fast path for block agreement, which reduces agreement latency to two communication rounds under optimistic conditions, with a fallback to the original Asynchronous Binary Agreement (ABA) in less favorable cases. However, this introduces another challenge: ensuring consistency between blocks committed via the fast and normal paths. To solve these challenges, we introduce a block retrieval mechanism and a preparation step, ensuring both liveness and consistency. Our experimental results show that Vega significantly outperforms existing protocols, reducing latency by up to 45% and achieving up to 1.8x higher throughput compared to HoneybadgerBFT.
{"title":"Vega: An Asynchronous BFT With Lower Communication Overhead and Lower Latency","authors":"Qichuan Liang;Rui Hao;Junhong Liu;Lijun Chen;Jiaao Tang;Xia Xie","doi":"10.1109/TNSE.2026.3655638","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3655638","url":null,"abstract":"The low-altitude economy has experienced significant growth recently, due to the flexibility, affordability, and versatility of low-altitude aircraft. A typical setup uses multiple devices in a distributed system, where the <italic>Byzantine Fault Tolerance</i> (BFT) consensus ensures data consistency. However, existing BFT protocols, such as those based on the BKR paradigm with HoneybadgerBFT as a representative, suffer from high communication overhead and latency, limiting scalability and performance. In this paper, we introduce Vega, a novel BFT protocol designed to overcome these challenges. Vega replaces the traditional <italic>Reliable Broadcast</i> (RBC) protocol with a more efficient linear <italic>Consistent Broadcast</i> (CBC), reducing communication overhead from <inline-formula><tex-math>$O(n^{3})$</tex-math></inline-formula> to <inline-formula><tex-math>$O(n^{2})$</tex-math></inline-formula>. However, this change introduces a new challenge related to totality, which may impact liveness. Additionally, Vega incorporates a fast path for block agreement, which reduces agreement latency to two communication rounds under optimistic conditions, with a fallback to the original <italic>Asynchronous Binary Agreement</i> (ABA) in less favorable cases. However, this introduces another challenge: ensuring consistency between blocks committed via the fast and normal paths. To solve these challenges, we introduce a block retrieval mechanism and a <italic>preparation</i> step, ensuring both liveness and consistency. Our experimental results show that Vega significantly outperforms existing protocols, reducing latency by up to 45% and achieving up to 1.8x higher throughput compared to HoneybadgerBFT.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6521-6537"},"PeriodicalIF":7.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175740","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}
Pub Date : 2026-01-19DOI: 10.1109/TNSE.2026.3655675
Meilin Xu;Min Jia;Yuyan Ren;Qing Guo;Tomaso de Cola
With the widespread deployment of low earth orbit (LEO) satellite networks, their high dynamism and large-scale introduce new challenges for the management and control of network communication resources and service orchestration. To tackle these challenges, this paper leverages software defined networking (SDN) and Network Function Virtualization (NFV) to the joint optimization of virtualized network function (VNF) deployment and request scheduling, referred to as the Joint VNF Deployment and Scheduling problem for Mobile Satellite Networks (JVDS-MSN). We formulate the JVDS-MSN problem as an Integer Linear Programming model with cross-timeslot service continuity constraints, aiming to minimize the end-to-end communication resource consumption. Given the NP-hard nature of the problem, we first propose an exact optimization method that integrates Dantzig-Wolfe decomposition with branch-and-bound techniques (DW-BP) to obtain optimal solutions. Although the proposed DW-BP algorithm yields high-quality solutions, its computational cost limits its applicability to large-scale scenarios. To address this, we propose a hierarchical reinforcement learning algorithm based on Twin Delayed Deep Deterministic Policy Gradient (HRL-TD3). This algorithm decomposes the VNF deployment and request scheduling tasks into high-level and low-level sub-tasks, thereby enabling more efficient optimization of bandwidth resources. Simulation results show that the proposed DW-BP algorithm efficiently computes optimal solutions, serving as a strong performance baseline. In large-scale and heterogeneous satellite network scenarios, the HRL-TD3 algorithm achieves near-optimal performance with significantly reduced computational overhead. Overall, the proposed method offers a promising solution for scalable and efficient service orchestration in mobile satellite networks.
{"title":"Joint Optimization of VNF Deployment and Request Scheduling in Mobile Satellite Networks","authors":"Meilin Xu;Min Jia;Yuyan Ren;Qing Guo;Tomaso de Cola","doi":"10.1109/TNSE.2026.3655675","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3655675","url":null,"abstract":"With the widespread deployment of low earth orbit (LEO) satellite networks, their high dynamism and large-scale introduce new challenges for the management and control of network communication resources and service orchestration. To tackle these challenges, this paper leverages software defined networking (SDN) and Network Function Virtualization (NFV) to the joint optimization of virtualized network function (VNF) deployment and request scheduling, referred to as the Joint VNF Deployment and Scheduling problem for Mobile Satellite Networks (JVDS-MSN). We formulate the JVDS-MSN problem as an Integer Linear Programming model with cross-timeslot service continuity constraints, aiming to minimize the end-to-end communication resource consumption. Given the NP-hard nature of the problem, we first propose an exact optimization method that integrates Dantzig-Wolfe decomposition with branch-and-bound techniques (DW-BP) to obtain optimal solutions. Although the proposed DW-BP algorithm yields high-quality solutions, its computational cost limits its applicability to large-scale scenarios. To address this, we propose a hierarchical reinforcement learning algorithm based on Twin Delayed Deep Deterministic Policy Gradient (HRL-TD3). This algorithm decomposes the VNF deployment and request scheduling tasks into high-level and low-level sub-tasks, thereby enabling more efficient optimization of bandwidth resources. Simulation results show that the proposed DW-BP algorithm efficiently computes optimal solutions, serving as a strong performance baseline. In large-scale and heterogeneous satellite network scenarios, the HRL-TD3 algorithm achieves near-optimal performance with significantly reduced computational overhead. Overall, the proposed method offers a promising solution for scalable and efficient service orchestration in mobile satellite networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6106-6121"},"PeriodicalIF":7.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082123","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}
Pub Date : 2026-01-19DOI: 10.1109/TNSE.2026.3655834
Gezahegn Abdissa Bayessa;Baida Zhang
In this research work, we consider the availability of mobile eavesdroppers and wardens, and investigate the covert and secure communication performance in UAV-ISAC-assisted D2D networks. To address the mobility challenges of eavesdroppers and wardens, we design Cramér-Rao Lower Bound (CRLB) threshold and the Extended Kalman Filter (EKF). We then frame the interaction between source devices, UAVs, wardens, and eavesdroppers, and formulate a Stackelberg game problem, where source devices and UAVs are leaders, and wardens and eavesdroppers are followers. We define the weighted sum of the interception rate of eavesdroppers and transmission detection error of wardens as a utility function, and formulate the joint eavesdroppers and wardens location, false alarm, and miss detection threshold optimization problem as a utility function maximization problem. We then formulate a joint transmission mode selection, UAV deployment, D2D pair association, communication, jamming, and sensing beamforming optimization problem as a long-term secure energy efficiency maximization problem. To address the followers problem, we propose a deep deterministic policy gradient (DDPG) algorithm. To obtain a strategy for leaders, we propose a hierarchical prompt decision-enhanced multi-agent transformer (HPD-MAT) algorithm with centralized attention multi-agent soft actor-critic (CAMA-SAC). Specifically, we design a shared encoder-independent decoder transformer with a CAMA-SAC. The simulation results demonstrate the effectiveness of the proposed algorithms.
{"title":"A Hierarchical Prompt-Enhanced Multi-Agent Transformer for Covert and Secure Communication Optimization in UAV-ISAC-Assisted D2D Networks","authors":"Gezahegn Abdissa Bayessa;Baida Zhang","doi":"10.1109/TNSE.2026.3655834","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3655834","url":null,"abstract":"In this research work, we consider the availability of mobile eavesdroppers and wardens, and investigate the covert and secure communication performance in UAV-ISAC-assisted D2D networks. To address the mobility challenges of eavesdroppers and wardens, we design Cramér-Rao Lower Bound (CRLB) threshold and the Extended Kalman Filter (EKF). We then frame the interaction between source devices, UAVs, wardens, and eavesdroppers, and formulate a Stackelberg game problem, where source devices and UAVs are leaders, and wardens and eavesdroppers are followers. We define the weighted sum of the interception rate of eavesdroppers and transmission detection error of wardens as a utility function, and formulate the joint eavesdroppers and wardens location, false alarm, and miss detection threshold optimization problem as a utility function maximization problem. We then formulate a joint transmission mode selection, UAV deployment, D2D pair association, communication, jamming, and sensing beamforming optimization problem as a long-term secure energy efficiency maximization problem. To address the followers problem, we propose a deep deterministic policy gradient (DDPG) algorithm. To obtain a strategy for leaders, we propose a hierarchical prompt decision-enhanced multi-agent transformer (HPD-MAT) algorithm with centralized attention multi-agent soft actor-critic (CAMA-SAC). Specifically, we design a shared encoder-independent decoder transformer with a CAMA-SAC. The simulation results demonstrate the effectiveness of the proposed algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6345-6365"},"PeriodicalIF":7.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175645","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}
Pub Date : 2026-01-16DOI: 10.1109/TNSE.2026.3654756
Yuxing Zhang;Lingling Wang;Meng Li;Keke Gai;Jingjing Wang
Byzantine-robust Federated Learning (FL) enables service providers to learn an accurate global model, even when some participants may be malicious. Existing Byzantine-robust FL approaches primarily rely on the service provider conducting statistical analysis on clients’ model updates, filtering out anomalous ones before aggregation to refine the global model. However, these defenses struggle to distinguish benign outliers from anomalous model updates under Byzantine attacks and heterogeneous settings, thereby harming model generalization ability. To address this issue, we propose a Byzantine-robust aggregation scheme based on hybrid anomaly detection (HadAGG) in heterogeneous FL. Specifically, we introduce a hybrid filtering strategy combining cosine similarity and Shapley values to distinguish between benign, malicious, and anomalous but benign model updates. To effectively identify benign outliers, we propose a Shapley value-based approach by constructing a multi-objective utility function that integrates the loss function and model accuracy to compute the Federated Shapley value, which measures client contributions. To achieve Byzantine-robust aggregation, we correct malicious model updates via gradient projection instead of directly discarding them, and employ a weighted aggregation to ensure that all model updates have a positive effect on model performance. Finally, we perform a theoretical analysis and a comprehensive evaluation for our scheme. Experimental results show that HadAGG outperforms existing state-of-the-art (SOTA) Byzantine-robust aggregation methods under different attack scenarios.
{"title":"“Malicious or Benign?”: Enhancing the Contribution of Model Updates in Byzantine-Robust Heterogeneous Federated Learning","authors":"Yuxing Zhang;Lingling Wang;Meng Li;Keke Gai;Jingjing Wang","doi":"10.1109/TNSE.2026.3654756","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3654756","url":null,"abstract":"Byzantine-robust Federated Learning (FL) enables service providers to learn an accurate global model, even when some participants may be malicious. Existing Byzantine-robust FL approaches primarily rely on the service provider conducting statistical analysis on clients’ model updates, filtering out anomalous ones before aggregation to refine the global model. However, these defenses struggle to distinguish benign outliers from anomalous model updates under Byzantine attacks and heterogeneous settings, thereby harming model generalization ability. To address this issue, we propose a Byzantine-robust aggregation scheme based on hybrid anomaly detection (HadAGG) in heterogeneous FL. Specifically, we introduce a hybrid filtering strategy combining cosine similarity and Shapley values to distinguish between benign, malicious, and anomalous but benign model updates. To effectively identify benign outliers, we propose a Shapley value-based approach by constructing a multi-objective utility function that integrates the loss function and model accuracy to compute the Federated Shapley value, which measures client contributions. To achieve Byzantine-robust aggregation, we correct malicious model updates via gradient projection instead of directly discarding them, and employ a weighted aggregation to ensure that all model updates have a positive effect on model performance. Finally, we perform a theoretical analysis and a comprehensive evaluation for our scheme. Experimental results show that HadAGG outperforms existing state-of-the-art (SOTA) Byzantine-robust aggregation methods under different attack scenarios.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6027-6040"},"PeriodicalIF":7.9,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082007","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}
Cyber attacks pose serious threats to computer systems. Automatically detecting anomalous patterns in system logs is critical for identifying and mitigating security risks. However, as log data grows increasingly complex and labeled logs remain scarce, existing detection methods face significant challenges. To address these issues, we introduce the pre-training and fine-tuning paradigm for log analysis and propose a hybrid pipeline tailored for accurate and low-cost log anomaly detection. Specifically, we employ a masked log reconstruction strategy to pre-train a Transformer encoder–based foundation model by leveraging the sequential dependencies in unlabeled logs. The model is then fine-tuned on an event prediction task to derive the anomaly detector. To reduce computational and storage overhead, we further design a knowledge distillation method tailored for compressing log anomaly detectors. Beyond fitting the detector's outputs, our method also exploits its internal representations to transfer richer knowledge. Experiments on the HDFS, BGL, and Thunderbird public datasets demonstrate that our framework outperforms state-of-the-art baselines in multiple metrics. Empirical evaluation on a reconstructed HDFS dataset confirms that it can adapt to real-world scenarios where labeled data is scarce. Moreover, through our knowledge distillation approach, the lightweight detectors achieve outstanding performance with substantially lower overhead, while maintaining robustness in real-world scenarios.
{"title":"Log Anomaly Detection via Transformers Pre-Trained on Massive Unlabeled Data","authors":"Senming Yan;Lei Shi;Jing Ren;Wei Wang;Limin Sun;Wei Zhang","doi":"10.1109/TNSE.2026.3654089","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3654089","url":null,"abstract":"Cyber attacks pose serious threats to computer systems. Automatically detecting anomalous patterns in system logs is critical for identifying and mitigating security risks. However, as log data grows increasingly complex and labeled logs remain scarce, existing detection methods face significant challenges. To address these issues, we introduce the pre-training and fine-tuning paradigm for log analysis and propose a hybrid pipeline tailored for accurate and low-cost log anomaly detection. Specifically, we employ a masked log reconstruction strategy to pre-train a Transformer encoder–based foundation model by leveraging the sequential dependencies in unlabeled logs. The model is then fine-tuned on an event prediction task to derive the anomaly detector. To reduce computational and storage overhead, we further design a knowledge distillation method tailored for compressing log anomaly detectors. Beyond fitting the detector's outputs, our method also exploits its internal representations to transfer richer knowledge. Experiments on the HDFS, BGL, and Thunderbird public datasets demonstrate that our framework outperforms state-of-the-art baselines in multiple metrics. Empirical evaluation on a reconstructed HDFS dataset confirms that it can adapt to real-world scenarios where labeled data is scarce. Moreover, through our knowledge distillation approach, the lightweight detectors achieve outstanding performance with substantially lower overhead, while maintaining robustness in real-world scenarios.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5943-5960"},"PeriodicalIF":7.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082078","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}
Pub Date : 2026-01-14DOI: 10.1109/TNSE.2026.3654163
Long Luo;Yanan Huang;Xixi Chen;Yongsheng Zhao;Hongfang Yu;Schahram Dustdar
In-network aggregation (INA) accelerates gradient aggregation in distributed machine learning (DML) by alleviating communication bottlenecks, but its effectiveness crucially depends on two location decisions: where to deploy INA functions and where to aggregate gradient flows. Most existing methods optimize INA placement and gradient flow routing independently, missing the advantages of joint optimization. This paper presents LLMINA, which leverages Large Language Models (LLMs) to automate the heuristic design for joint INA placement and gradient aggregation, aiming to minimize makespan (i.e., the total time required for all DML jobs to complete gradient aggregation). Directly using LLMs to generate end-to-end solutions is infeasible due to problem complexity and LLM limitations. Instead, LLMINA uses LLMs to generate heuristics for INA placement through an evolutionary process, and then applies an optimization-based heuristic for gradient routing that takes into account DML workload characteristics. Experiments across diverse network topologies and workloads show that LLMINA can significantly reduce makespan compared to state-of-the-art baselines. These results underscore that location matters for both INA deployment and aggregation, and highlight the potential of LLM-guided heuristic design for complex network resource optimization.
{"title":"Location Matters: LLM-Guided Joint Optimization of In-Network Aggregation Placement and Routing for DML Workloads","authors":"Long Luo;Yanan Huang;Xixi Chen;Yongsheng Zhao;Hongfang Yu;Schahram Dustdar","doi":"10.1109/TNSE.2026.3654163","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3654163","url":null,"abstract":"In-network aggregation (INA) accelerates gradient aggregation in distributed machine learning (DML) by alleviating communication bottlenecks, but its effectiveness crucially depends on two location decisions: where to deploy INA functions and where to aggregate gradient flows. Most existing methods optimize INA placement and gradient flow routing independently, missing the advantages of joint optimization. This paper presents LLMINA, which leverages Large Language Models (LLMs) to automate the heuristic design for joint INA placement and gradient aggregation, aiming to minimize makespan (i.e., the total time required for all DML jobs to complete gradient aggregation). Directly using LLMs to generate end-to-end solutions is infeasible due to problem complexity and LLM limitations. Instead, LLMINA uses LLMs to generate heuristics for INA placement through an evolutionary process, and then applies an optimization-based heuristic for gradient routing that takes into account DML workload characteristics. Experiments across diverse network topologies and workloads show that LLMINA can significantly reduce makespan compared to state-of-the-art baselines. These results underscore that location matters for both INA deployment and aggregation, and highlight the potential of LLM-guided heuristic design for complex network resource optimization.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5978-5991"},"PeriodicalIF":7.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081967","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}
Pub Date : 2026-01-14DOI: 10.1109/TNSE.2026.3654107
Runxiao Liu;Xiangli Le;Shuang Gu;Shuli Lv;Pengda Mao;Quan Quan
This paper presents a novel distributed network control framework for cooperative heavy-load transportation using multi-UAV systems, accounting for thrust limitations and heterogeneous cable characteristics. By constructing a virtual passive system comprising interconnected virtual nodes, springs, and dampers, the proposed method decouples internal coordination stability from external velocity tracking. A velocity tracking controller is devised to asymptotically steer the load’s velocity toward a desired trajectory, while preserving inter-agent cohesion through virtual interactions. Notably, the controller operates without explicit inter-UAV communication, relying solely on relative position measurements. Numerical simulations involving ten UAVs transporting a 14 kg load-exceeding 76% of their combined thrust capacity-along a figure-eight trajectory validate the proposed method. Field tests with six UAVs transporting a 6 kg load are conducted to validate the control framework’s performance in practical applications. The results confirm accurate velocity tracking, balanced cable tension distribution, and scalability to heterogeneous UAV team configurations.
{"title":"Distributed Network Control of Multi-UAV Systems for Cooperative Heavy-Load Transport Using a Virtual-Passivity Framework","authors":"Runxiao Liu;Xiangli Le;Shuang Gu;Shuli Lv;Pengda Mao;Quan Quan","doi":"10.1109/TNSE.2026.3654107","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3654107","url":null,"abstract":"This paper presents a novel distributed network control framework for cooperative heavy-load transportation using multi-UAV systems, accounting for thrust limitations and heterogeneous cable characteristics. By constructing a virtual passive system comprising interconnected virtual nodes, springs, and dampers, the proposed method decouples internal coordination stability from external velocity tracking. A velocity tracking controller is devised to asymptotically steer the load’s velocity toward a desired trajectory, while preserving inter-agent cohesion through virtual interactions. Notably, the controller operates without explicit inter-UAV communication, relying solely on relative position measurements. Numerical simulations involving ten UAVs transporting a 14 kg load-exceeding 76% of their combined thrust capacity-along a figure-eight trajectory validate the proposed method. Field tests with six UAVs transporting a 6 kg load are conducted to validate the control framework’s performance in practical applications. The results confirm accurate velocity tracking, balanced cable tension distribution, and scalability to heterogeneous UAV team configurations.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5905-5923"},"PeriodicalIF":7.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082081","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}
Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model – the Transformer – is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.
{"title":"Intelligent Angle Map-Based Beam Alignment for RIS-Aided mmWave Communication Networks","authors":"Hao Xia;Qing Xue;Yanping Liu;Binggui Zhou;Meng Hua;Qianbin Chen","doi":"10.1109/TNSE.2026.3653564","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653564","url":null,"abstract":"Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model – the Transformer – is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5833-5850"},"PeriodicalIF":7.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026387","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}
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