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Gradient sparsification for efficient wireless federated learning with differential privacy

Science China(information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU school of electronic and Optical engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation school of information and Communication engineering Hainan University school of Communications and information engineering Nanjing University of Posts and Telecommunications school of electronic information and Electrical engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client’s local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client’s DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua school of information engineering East China Jiaotong UniversityNanchang 330013China school of electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related *** basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location ***,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy *** this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting ***,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it ***,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are ***,extensive simulation results are *** is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

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Deployment of Edge Computing Nodes in IoT:Effective Implementation of Simulated Annealing Method Based on User Location

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China Communications 2024年第1期21卷 279-296页

作者： Junhui Zhao Ziyang Zhang Zhenghao Yi Xiaoting Ma Qingmiao Zhang school of electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China school of information engineering East China Jiaotong UniversityNanchang 330013China

Edge computing paradigm for 5G architecture has been considered as one of the most effective ways to realize low latency and highly reliable communication,which brings computing tasks and network resources to the edge of *** deployment of edge computing nodes is a key factor affecting the service performance of edge computing *** this paper,we propose a method for deploying edge computing nodes based on user *** the combination of Simulation of Urban Mobility(SUMO)and Network Simulator-3(NS-3),a simulation platform is built to generate data of hotspot areas in Io T *** effectively using the data generated by the communication between users in Io T scenario,the location area of the user terminal can be *** this basis,the deployment problem is expressed as a mixed integer linear problem,which can be solved by Simulated Annealing(SA)*** analysis of the results shows that,compared with the traditional method,the proposed method has faster convergence speed and better performance.

关键词： deployment problem edge computing internet of things machine learning

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CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

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Computers, Materials & Continua 2024年第8期80卷 2761-2783页

作者： Nannan Chong Fan Yang school of electronic and information engineering Hebei University of TechnologyTianjin300401China school of information and Intelligence engineering Tianjin Renai CollegeTianjin301636China

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural ***,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and *** can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for *** address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of ***,a novel lightweight feature extraction and matching network based on attention mechanism is *** network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese *** extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur *** and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level *** ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic *** results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage *** compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense ***

关键词： Feature extraction and matching lightweighted network medical images endoscopic attention

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A Probing Model of Secret Key Generation Based on Channel Autocorrelation Function

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China Communications 2024年第6期21卷 163-175页

作者： Xia Enjun Hu Binjie Shen Qiaoqiao school of electronic and information engineering South China University of TechnologyGuangzhou 510641China

Secret key generation(SKG)is a promising solution to the problem of wireless communications *** the first step of SKG,channel probing affects it *** there have been some probing schemes,there is a lack of research on the optimization of the probing *** study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)***,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample *** on the model,the analytical expression of the SKGR is ***,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve *** conclude the SKGR monotonically increases as the transmitting power *** mathematical proofs are given in this *** the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.

关键词： channel autocorrelation function channel probing optimization problem physical layer security secret key generation

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Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2111-2122页

作者： Zizhang Qiu Shouguang Wang Dan You MengChu Zhou school of information and electronic engineering Zhejiang Gongshang UniversityHangzhou 310018China IEEE

Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and *** computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of *** this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search *** experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective *** performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

关键词： Contract Bridge reinforcement learning search

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HetNets Under Decoupled Uplink and Downlink Access with UE Random Discontinuous Transmission: Local Delay and Energy Efficiency

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China Communications 2024年第3期21卷 205-216页

作者： Zhang Yue Zheng Fuchun Luo Jingjing school of electronic and information engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

Due to the limited uplink capability in heterogeneousnetworks (HetNets), the decoupled uplinkand downlink access (DUDA) mode has recently beenproposed to improve the uplink performance. In thispaper, the random discontinuous transmission (DTX)at user equipment (UE) is adopted to reduce the interferencecorrelation across different time slots. By utilizingstochastic geometry, we analytically derive themean local delay and energy efficiency (EE) of an uplinkHetNet with UE random DTX scheme under theDUDA mode. These expressions are further approximatedas closed forms under reasonable *** results reveal that under the DUDA mode, there isan optimal EE with respect to mute probability underthe finite local delay constraint. In addition, with thesame finite mean local delay as under the coupled uplinkand downlink access (CUDA) mode, the HetNetsunder the DUDA mode can achieve a higher EE witha lower mute probability.

关键词： decoupled uplink and downlink access(DUDA) heterogeneous networks(HetNets) random discontinuous transmission(DTX) stochastic geometry

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Novel self-embedding holographic watermarking image encryption protection scheme

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Chinese Physics B 2024年第5期33卷 281-290页

作者：王励年周楠润孙博曹颖鸿牟俊 school of information Science and engineering Dalian Polytechnic UniversityDalian 116034 China school of electronic and Electrical engineering Shanghai University of Engineering ScienceShanghai 201620China

For digital image transmission security and information copyright,a new holographic image self-embedding watermarking encryption scheme is ***,the plaintext is converted to the RGB three-color channel,the corresponding phase hologram is obtained by holographic technology and the watermark is self-embedded in the frequency ***,by applying the Hilbert transform principle and genetic center law,a complete set of image encryption algorithms is constructed to realize the encryption of image ***,simulation results and security analysis indicate that the scheme can effectively encrypt and decrypt image information and realize the copyright protection of *** introduced scheme can provide some support for relevant theoretical research,and has practical significance.

关键词： color image encryption Hilbert transform self-embedding watermark holographic technology

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Long-term variation of Arctic Sudden Stratospheric Warmings(SSW) and potential causes

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Earth and Planetary Physics 2024年第5期8卷 742-752页

作者： QingRan Li ShaoDong Zhang KaiMing Huang ChunMing Huang Yun Gong WenTao Tang Zheng Ma school of electronic information Wuhan University Guizhou Normal University Key Laboratory of Geospace Environment and Geodesy Ministry of Education State Key Laboratory of information engineering in Surveying Mapping and Remote Sensing school of electronic information engineering Jingchu University of Technology

Utilizing the European Centre for Medium-Range Weather Forecast(ECMWF) Reanalysis v5(ERA5), for the first time, we have confirmed close links among Sudden Stratospheric Warmings(SSWs) in the Northern Hemisphere(NH), the polar vortices, and stratospheric Planetary Waves(PWs) by analyzing and comparing their trends. Interestingly, within overall increasing trends, the duration and strength of SSWs exhibit increasing and decreasing trends before and after the winter of 2002, respectively. To reveal possible physical mechanisms driving these trends, we analyzed the long-term trends of the winter(from December to February) polar vortices and of stratospheric PWs with zonal wave number 1. Notably, our results show that in all three time periods(the entire period of 41winters, 1980 to 2020, and the two subperiods — 1980-2002 and 2002-2020) enhancing SSWs were always accompanied by weakening winter polar vortices and strengthening polar PWs like Stationary Planetary Waves(SPWs) and 16-day waves, and vice versa. This is the first proof, based on ERA5 long-term trend data, that weakening polar vortices and enhancing stratospheric PWs(especially SPWs) could cause an increase in SSWs.

关键词： sudden stratospheric warmings stationary planetary waves 16-day waves polar vortrices long-term trend correlation coefficient

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Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

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Computers, Materials & Continua 2024年第7期80卷 527-546页

作者： Amna Khatoon Weixing Wang Asad Ullah Limin Li Mengfei Wang school of information engineering Chang’an UniversityXi’an710064China school of information engineering Xi’an Eurasia UniversityXi’an710065China school of Electrical and electronic engineering Wenzhou UniversityWenzhou325035China

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader *** devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning *** modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in *** showcases its superiority over conventional detection algorithms in different road image *** constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational *** Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge *** analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

关键词： Edge computing remote sensing TinyML optimization BNNs road anomaly detection quantization model compression

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