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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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3D Physical Layer Secure Transmission for UAV-Assisted Mobile Communications Without Locations of Eavesdroppers 18th

3D Physical Layer Secure Transmission for UAV-Assisted Mobi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Yu, Wenlu Li, Jianqiang Fan, Xin Wang, Guopeng Li, Guangkai Luo, Chuanwen Hong, Yi Chen, Ting School of Information Science and Technology Beijing Forestry University Beijing100083 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029 China Zhejiang Ocean University Zhoushan316022 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783031714665

The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in a wide range of scenarios have made them indispensable tools for mobile communications. However, UAV communications are also facing increasing security threats, especially in open spaces where they are vulnerable to potential eavesdroppers. In this paper, we employ an extra UAV as a cooperative jammer to enhance UAV-to-ground communication security by sending artificial noise to effectively resist passive eavesdroppers. Unlike previous work, we focus on the dynamic scenario where the source UAV and the ground user move simultaneously. In this scenario, we propose a joint optimization algorithm of trajectory and power for the jamming UAV to obtain the optimal jamming strategy. Due to the absence of eavesdroppers’ locations, the goal of the optimization is to maximize a secure region. Numerical results demonstrate that our optimization algorithm can significantly enlarge the secure region compared to randomly selecting a position of the jamming UAV. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Jamming

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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An Efficient Compaction Scheme for LSM-Based Storage System Via Lightweight Merges

An Efficient Compaction Scheme for LSM-Based Storage System ...

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2024 International Conference Optoelectronic Information and Optical engineering, OIOE 2024

作者： Fan, Shuhuan Liu, Yubo Xi, Rui Mu, Zepei Hou, Mengshu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China China Jiuyuan Hi-Tech Equipment Corp. Mianyang China Network Technology Co Ltd. Chengdu China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the development of information technology, industry 4.0, mobile applications, etc., the volume of data has shown explosive growth, NoSQL databases are widely used to support data storage and access services. To resolve the write amplification problem for the common storage architecture LSM-Tree, this paper proposes a novel merge scheme, namely LSMA-Tree, by combining the reading technique of leveling scheme and the writing technique of tiering scheme. For each layer of LSM-Tree, this paper maintains the order of the whole key by files like leveling scheme. Furthermore, in the merging stage of the upper and lower files, LSMA-Tree adopts the "append" rather than "reorganization" method to avoid the read and write operations of the lower files, thereby reducing the number of disk IOs. This paper also designs a new sstable file structure to adapt to the proposed LSMA scheme, and implements and verifies it on LevelDB. Experimental results show that the proposed LSMA scheme can effectively reduce write amplification and improve write performance by more than 50%, while the impact on read performance is less than 5%. © 2025 SPIE.

关键词： Compaction

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China Information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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Resource allocation in UAV assisted air ground intelligent inspection system

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Cognitive Robotics 2022年 2卷 1-12页

作者： Zhang, Zhuoya Xu, Fei Qin, Zengshi Xie, Yue School of ordnance science and technology Xi'an Technological University Xi'An China Engineering Laboratory The State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'An China School of Computer Science and Engineering Xi'an Technological University Xi'An China

With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mobile edge computing provides a promising architecture to meet the explosive growth of communication and computing needs of inspection robot. Inspection robot can transmit the collected High Definition (HD) video to adjacent edge servers for data processing and state research and judgment. However, the communication constraints of long-distance transmission, high reliability and low delay pose challenges to task offloading optimization. Therefore, this paper introduced Unmanned Aerial Vehicle (UAV) and established UAV assisted mobile edge computing system. UAV assisted and mobile edge computing are combined to form edge computing nodes. In this way, it provided communication and computing services to the IR for fast data processing. Specifically, in order to optimize the system energy consumption, a resource allocation strategy based on genetic algorithm is proposed. By optimizing the offloading decision and computing resource allocation of the IRs, the computing task of the IRs are offloaded to an energy-efficient UAV. The experimental results show that the resource allocation strategy based on genetic algorithm can effectively reduce the energy consumption and cost of UAVs and IRs, and effectively realize the reasonable allocation of resources. The results verify the effectiveness and reliability of the algorithm in the real scene. © 2021

关键词： Resource allocation

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Progressive edge-sensing dynamic scene deblurring

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Computational Visual Media 2022年第3期8卷 495-508页

作者： Tianlin Zhang Jinjiang Li Hui Fan School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Institute of ZhongKe Network Technology Yantai 264005China

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many *** recent years,the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively *** have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data *** new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred *** coarse-to-fine architecture restores the image structure,first performing global adjustments,and then performing local *** this way,not only is global correlation considered,but also residual information is used to significantly improve image restoration and enhance texture *** results show quantitative and qualitative improvements over existing methods.

关键词： image deblurring dynamic scenes multiscale edge features

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A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

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computers, Materials & Continua 2023年第1期74卷 1279-1302页

作者： Yu Fan Jingbing Li Uzair Aslam Bhatti Chunyan Shao Cheng Gong Jieren Cheng Yenwei Chen School of Information and Communication Engineering Hainan UniversityHaikou570100China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570100China School of Computer Science and Technology Hainan UniversityHaikou570100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570100China TJ-YZ School of Network Science Haikou University of EconomicsHaikou571127China

Medical images are a critical component of the diagnostic process for *** the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical *** watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient *** addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are *** paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned ***,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution ***,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical *** last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third *** encrypted watermarks and the original image features are performed logical operations to realize the embedding of *** the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed *** NC values for all the images are more than 90%accurate which shows the robustness of the *** experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

关键词： Inception V3 multi-watermarking DCT watermark encryption robustness

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Knowledge Extraction and Application of Railway Operation Equipment Fault Diagnosis Based on Knowledge Graph 15

Knowledge Extraction and Application of Railway Operation Eq...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： He, Ruiyi Li, Honghui Liu, Shufang Yang, Xiaorui Wang, Zibo School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China School of Railway Safety Theory and Technology Southwest Jiaotong University Sichuan China

ISBN: (纸本)9798350354010

Focusing on the issue where traditional fault diagnosis methods cannot effectively utilize the textual records of railway equipment faults and heavily rely on experts' experience, a knowledge graph-based fault analysis method for railway operation equipment is proposed. By designing preprocessing rules to process the text corpus to be annotated, and combining the attention mechanism (ATT) and bi-directional gated recurrent neural network (BiGRU) to complete the knowledge extraction, the knowledge graph of railway operation equipment faults is constructed. On this basis, a cause recommendation strategy combining query and reasoning is designed, which can give reasonable fault analysis suggestions based on the faults that have occurred. This research utilizes the dataset sourced from a railway administration, examines the effectiveness of the knowledge extraction method through experiments, and analyzes the feasibility of utilizing the constructed knowledge graph to assist in fault analysis through cause recommendation. © 2024 IEEE.

关键词： Knowledge graph

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