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数字通信与网络（英文）
数字通信与网络（英文）

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数字通信与网络（英文）/CSCD北大核心SCI
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    Suitability of SDN and MEC to facilitate digital twin communication over LTE-A

    Hikmat AdhamiMohammad Alja'afrehMohamed HodaJiaqi Zhao...
    347-354页
    查看更多>>摘要：Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications.This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet.While the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin.This work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges.Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and jitter.The best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per second.The results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    PEPFL:A framework for a practical and efficient privacy-preserving federated learning

    Yange ChenBaocang WangHang JiangPu Duan...
    355-368页
    查看更多>>摘要：As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users'original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosys-tem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

    Héber H.ArcoleziJean-Fran?ois CouchotBechara Al BounaXiaokui Xiao...
    369-379页
    查看更多>>摘要：This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the"multiple"settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response-GRR,Optimized Unary Encoding-OUE,and Symmetric Unary Encoding-SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of saniti-zation(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidi-mensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

    Renwan BiMingfeng ZhaoZuobin YingYouliang Tian...
    380-388页
    查看更多>>摘要：With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)al-gorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theo-retical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63％and 23％higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

    Lihua YinSixin LinZhe SunRan Li...
    389-403页
    查看更多>>摘要：Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent chal-lenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players'decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

    Mohamed Abdel-BassetHossam HawashNour MoustafaImran Razzak...
    404-415页
    查看更多>>摘要：With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3％-8％as accuracy improvements.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Data complexity-based batch sanitization method against poison in distributed learning

    Silv WangKai FanKuan ZhangHui Li...
    416-428页
    查看更多>>摘要：The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Joint alignment and steering to manage interference

    Zhao LiXiujuan LiangYinghou LiuJia Liu...
    429-438页
    查看更多>>摘要：In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network perfor-mance.However,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)exclusively.It is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each interference.To remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in IM.With IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference.Moreover,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are presented.The former considers the influence of IA on the interfering user-pair's performance.The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the inter-fering communication pairs.Under ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered communication-pairs.Since the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both communication-pairs.Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

    Chaopeng GuoZhaojin ZhongZexin ZhangJie Song...
    439-449页
    查看更多>>摘要：A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy pre-diction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy predic-tion.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that Neu-rstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60％.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83％over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Video caching and scheduling with edge cooperation

    Zhidu LiFuxiang LiTong TangHong Zhang...
    450-460页
    查看更多>>摘要：In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.
      原文链接:
    • NETL
    • NSTL
    • 万方数据