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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Gao, Zhipeng Li, Yichen Yu, Xinlei The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Communications Beijing China

ISBN: (纸本)9789819615278

Large language models (LLMs) trained on massive publicly accessible data excel in general domain tasks. However, the performance of LLMs in specialized domains such as healthcare and mathematics frequently fails to meet established benchmarks. The primary obstacle to improving this performance is the scarcity of high-quality and domain-specific data due to stringent data privacy regulations. Federated learning, which facilitates the training of LLMs across distributed devices through a decentralized approach that precludes centralized data aggregation presents a potential solution. Nevertheless, federated learning for instruction tuning encounters challenges including data heterogeneity and substantial computational resource requirements. Consequently, this paper proposes FEDNPAIT, an innovative federated learning framework for instruction tuning that incorporates Nesterov-accelerated momentum and a new parameter for controlling the adaptive strength of gradient updates during optimization to improve the performance of LLMs in heterogeneous federated learning environments. Furthermore, FEDNPAIT effectively addresses computational resource limitations through the incorporation of Low-Rank Adaptation. Experimental results indicate that FEDNPAIT effectively mitigates the adverse effects of data heterogeneity in federated environments and outperforms existing federated learning algorithms. Specifically, the results from tuned Llama3-8B demonstrate enhanced generalization and robustness, illustrating how to incorporate complex optimization algorithms into federated training of LLMs, and opening new possibilities for federated instruction tuning in sensitive industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission

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IEEE Communications Letters 2025年第5期29卷 898-902页

作者： Yue, Weijie Si, Zhongwei Wu, Bolin Wang, Sixian Qin, Xiaoqi Niu, Kai Dai, Jincheng Zhang, Ping Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature's different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions. © 1997-2012 IEEE.

关键词： Channel coding

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Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-Based Approach

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1259-1275页

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing Hefei University of Technology School of Computer Science and Information Engineering China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Macau University of Science and Technology Faculty of Information Technology China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. © 2004-2012 IEEE.

关键词： Level measurement

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SecFFT: Safeguarding Federated Fine-Tuning for Large Vision Language Models Against Covert Backdoor Attacks in IoRT Networks

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IEEE Internet of Things Journal 2025年第9期12卷 11383-11396页

作者： Zhou, Zan Xu, Changqiao Wang, Bo Li, Tengfei Huang, Sizhe Yang, Shujie Yao, Su Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100190 China

As the large vision language models (LVLMs) and embodied intelligent robotic networks continue to advance at a remarkable pace, particularly in applications spanning smart cities, power grids, factories, and transportation, visual perception and understanding have emerged as foundational elements to overcoming performance limitations in such intelligent systems. However, since general pretrained models are not well-suited to specific tasks, federated fine-tuning (FFT) has gained attention as a promising technique for enhancing the performance of vision-based perception models by leveraging data and computational power distributed across nodes. The rise of advanced persistent threats has revealed significant vulnerabilities in existing defense mechanisms, which struggle to mitigate sophisticated backdoor attacks toward FFT for LVLMs. To address these challenges, this article proposes the SecFFT method, which tackles both the stealthiness and complexity of backdoor strategies. The approach incorporates instantaneous attack behavior detection based on frequency-domain distribution consistency and introduces a long-term secure aggregation mechanism aimed at identifying hidden attack intentions. These strategies effectively limit the feasibility of adversaries attempting to bypass defense measures by concealing their behaviors. Experiments conducted on public datasets demonstrate that SecFFT significantly improves defense success rates, model performance, and detection accuracy, particularly in response to highly covert, multiround backdoor attacks. © 2014 IEEE.

关键词： Intelligent systems

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Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid Network

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Computers, Materials & Continua 2025年第5期83卷 2429-2447页

作者： Qinghai Ou Min Yang Jingcai Kong Yang Yang Beijing FibrLink Communications Co.Ltd. Beijing100071China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid *** such cases,a space-air-ground integrated network serves as an effective emergency *** study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity *** to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop *** the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory *** first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)*** iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop *** results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.

关键词： Energy efficiency effective capacity delay requirement power allocation smart grid space-air-ground integrated network

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Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection with Decentralized DRL in 6G-Enabled Space-Air-Ground Networks

Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Ru Lin, Rongheng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

As sixth-generation (6G) networks extend to remote regions, efficient data collection from Internet of Things (IoT) devices faces challenges such as limited infrastructure, diverse latency requirements, and energy constraints. To address these, we propose a dual-mode data transmission strategy: (1) a UAV-satellite network for real-time delay-sensitive data, where wireless power transfer (WPT) is introduced to alleviate energy depletion caused by frequent data transmission, and a deadline τmax is set in each timeslot to maintain data freshness. (2) a carry-store mode for delay-tolerant data. Both aim to minimize energy consumption while maximizing data collection. Specifically, we develop a time scheduling policy to allocate data and energy transmission time for delay-sensitive data within τmax, and propose a novel centralized control, distributed execution framework using decentralized deep reinforcement learning (DRL), termed DRL-Schedule, designed to optimize UAV navigation and ensure energy-efficient, timely data collection. Simulation results demonstrate that DRL-Schedule achieves an average improvement of approximately 11.2% and 12.8% over the best baseline PPO in energy efficiency, when varying different number of UAVs and IoTs, respectively, and notably surpassing GA, Greedy and Random. © 2025 IEEE.

关键词： 6G Deep reinforcement learning Energy-efficiency UAV-LEO integrated data collection WPT

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LEP: Leveraging Local Entropy Pruning for Sparsity in Large Language Models

LEP: Leveraging Local Entropy Pruning for Sparsity in Large ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yuli Cheng, Bo Zhang, Yingying Zhang, Shuhao Wu, Zhixuan Meng, Fanshen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

The application of Large Language Models (LLMs) is rapidly expanding in fields such as natural language processing and computer vision. However, due to the enormous number of model parameters, while their emergent capabilities enhance performance, they also incur significant computational and storage costs, particularly during inference. This makes it particularly challenging to deploy large models in resource-constrained environments. To address this issue, we propose an innovative pruning method based on local entropy, aimed at improving model performance while reducing computational costs. Our approach leverages a small amount of calibration data and calculates the importance of weights based on the local entropy of weights and input activations, applying it to both unstructured and semi-structured pruning without the need for complex weight updates or retraining. Experimental results demonstrate that our method shows superior performance across models of various scales. Specifically, at a sparsity rate of 50%, our method significantly reduces model perplexity without the need for weight updates and outperforms existing pruning techniques such as SparseGPT and Wanda in zero-shot tasks. The code is available at: this https URL. © 2025 IEEE.

关键词： entropy large language models model compression unstructed pruning

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Atlas: Towards Real-Time Verification in Large-Scale Networks via a Native Distributed Architecture 25

Atlas: Towards Real-Time Verification in Large-Scale Network...

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20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Ma, Mingxiao Zhang, Yuehan Wang, Jingyu He, Bo Zhao, Chenyang Qi, Qi Zhuang, Zirui Sun, Haifeng Guo, Lingqi Guo, Yuebin Zhang, Gong Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Pengcheng Laboratory China *** China Huawei Technologies China

ISBN: (纸本)9798400711961

Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations;(2) scalable so as to accommodate today’s large-scale network architecture. Current DPV tools struggle to meet these requirements due to their centralized architecture. To be concrete, there is a bottleneck for a single-point server to perform real-time DPV tasks. Furthermore, a single-point server makes it hard to collect real-time data plane updates from every device in large-scale networks. This paper proposes Atlas, a native distributed data plane verification (DPV) solution, which systematically solves its scalability problem and improves its real-time performance. To achieve this, (1) Atlas designs a hierarchical distributed architecture for DPV in large-scale networks. (2) Atlas proposes a native approach to maximize the parallelism of the architecture. Both dataset-driven simulation experiments and deployments in the wild demonstrate that Atlas is capable of fast and scalable distributed verification. The advantages offered by Atlas continue to expand as network sizes increase. Compared to state-of-the-art solutions, Atlas is 2-7× faster while keeping each component lightweight. © 2025 Copyright held by the owner/author(s).

关键词： Packet loss

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Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13327-13339页

作者： Wang, Xudong Du, Hongyang Feng, Lei Zhou, Fanqin Li, Wenjing Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong

The development of B5G and 6G technologies has led to an explosive growth in device connectivity density in Industrial Internet of Things (IIoT) systems. However, the limited spectrum resources in industrial wireless networks pose significant challenges for large-scale access and communication rates, especially for factory automation applications that are sensitive to control stability and latency. In this paper, we investigate an uplink non-orthogonal multiple access (NOMA) transmission for ultra-reliable and low-latency communication (URLLC) services in IIoT systems, where sensors in NOMA clusters transmit collected data to the base station to meet the high communication rate and control stability requirements of controlled devices. The dynamic control convergence constraint is theoretically transformed into an optimal control condition in each communication round based on the decoding error probability. Additionally, we formulate an optimization problem to maximize the effective throughput of the considered system in the finite blocklength regime by jointly optimizing blocklength allocation, power allocation, and decoding error probability. To solve this mixed integer non-linear programming (MINLP) problem, we decompose it into two sub-problems and propose an efficient optimization framework based on generative AI. Specifically, we apply successive convex approximation (SCA) to solve the blocklength allocation sub-problem, and use a diffusion model to address the joint power control and decoding error probability sub-problem. Finally, extensive simulation results demonstrate the effectiveness of this approach. © 2014 IEEE.

关键词： Decoding

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Multivariate Time-Series Anomaly Detection Based on Dynamic Graph Neural Networks and Self-Distillation in Industrial Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 12181-12192页

作者： Zhao, Mengmeng Peng, Haipeng Li, Lixiang Beijing University of Posts and Telecommunications Information Security Center State Key Laboratory of Networking and Switching Technology National Engineering Laboratory for Disaster Backup and Recovery Beijing100876 China Zaozhuang University Department of Information Science and Engineering Shandong 277160 China

Time-series anomaly detection is critical to securing the Industrial Internet of Things (IIoT). Although numerous deep learning-based methods have been proposed, these methods fail to consider the interdependencies between different dimensions of the data and often neglect the dynamic changes in these dependencies. Moreover, these methods utilize only the global features from the last layer of the network for anomaly detection. However, local features can capture subtle variations in the data, which are crucial for accurately detecting anomalies. To alleviate these problems, this article proposes a novel framework for detecting time-series anomalies, including four parts, namely, the graph structure learning module, the dynamic graph module, the anomaly scoring module, and the self-distillation. The graph structure learning module generates different graph structures based on the inputs, which will be used in the dynamic graph module. The dynamic graph module employs dynamic graph neural networks to capture the complex relationships within time series from both temporal and spatial dimensions. The anomaly scoring module obtains anomaly scores from predictions and observed values, and the model makes anomaly judgments based on these scores. Additionally, self-distillation enhances model performance by utilizing mutual learning between the teacher and student models, thereby integrating local and global information for better anomaly detection. We carry out a series of experiments on IIoT datasets, which verify the performance of the framework. The experimental results of the proposed method outperform other methods, demonstrating the advantage of our framework. © 2014 IEEE.

关键词： Time series analysis Anomaly detection Feature extraction Graph neural networks Industrial Internet of Things Training Data models Adaptation models Transformers Telecommunications

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