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A survey of energy-efficient strategies for federated learning in mobile edge computing

Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 645-663页

作者： Kang YAN Nina SHU Tao WU Chunsheng LIU Panlong YANG school of Electronic Engineering National University of Defense TechnologyHefei 230009China Department of computing Hong Kong Polytechnic UniversityHong Kong 999077China school of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

With the booming development of fifth-generation network technology and Internet of Things,the number of end-user devices(EDs)and diverse applications is surging,resulting in massive data generated at the edge of *** process these data eficiently,the innovative mobile edge computing(MEC)framework has emerged to guarantee low latency and enable eficient computing close to the user ***,federated learning(FL)has demonstrated its empirical success in edge computing due to its privacy-preserving ***,it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks,which are the major workloads in ***,EDs are typically powered by batteries with limited capacity,which brings challenges when performing energy-intensive FL *** address these challenges,many strategies have been proposed to save energy in *** the absence of a survey that thoroughly summarizes and classifies these strategies,in this paper,we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in ***,we first introduce the system model and energy consumption models in FL,in terms of computation and *** we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives:learning-based,resource allocation,and client *** conduct a detailed analysis of these strategies,comparing their advantages and ***,we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental ***,several potential future research directions for energy-efficient FL are discussed.

关键词： Mobile edge computing Federated learning Energy-efficient

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Quantum search with prior knowledge

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Science China(Information Sciences) 2024年第9期67卷 321-329页

作者： Xiaoyu HE Xiaoming SUN Jialing ZHANG State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences school of Computer Science and Technology University of Chinese Academy of Sciences

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example.

关键词： quantum computing quantum search quantum query algorithm prior knowledge

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer Science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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GCAGA: A Gini Coefficient-Based Optimization Strategy for Computation Offloading in Multi-User-Multi-Edge MEC System

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Computers, Materials & Continua 2024年第6期79卷 5083-5103页

作者： Junqing Bai Qiuchao Dai Yingying Li school of computing Xi’an Shiyou UniversityXi’an710065China Product Center Wingtech Technology(Wuxi)Co.Wuxi214028China

To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s ***,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging *** address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this *** a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time *** EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading *** offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor ***,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the *** proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini *** simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher *** simulation code of the proposed algorithm is available:https://***/Grox888/Mobile_Edge_computing/tree/GCAGA.

关键词： Mobile edge computing multi-user-multi-edge joint optimization Gini coefficient adaptive genetic algorithm

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A Survey on the Evolution of Bootkits Attack and Defense Techniques

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China Communications 2024年第1期21卷 102-130页

作者： Yilin Zhou Guojun Peng Zichuan Li Side Liu Key Laboratory of Aerospace Information Security and Trust computing Ministry of EducationWuhan 430072China school of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

According to the boot process of modern computer systems,whoever boots first will gain control *** advantage of this feature,a malicious code called bootkit can hijack the control before the OS bootloader and bypass security mechanisms in boot *** makes bootkits difficult to detect or clean up *** the improvement of security mechanisms and the emergence of UEFI,the attack and defense techniques for bootkits have constantly been *** first introduce two boot modes of modern computer systems and present an attack model of bootkits by some sophistical *** we discuss some classic attack techniques used by bootkits from their initial appearance to the present on two axes,including boot mode axis and attack phase ***,we evaluate the race to the bottom of the system and the evolution process between bootkits and security *** last,we present the possible future direction for bootkits in the context of continuous improvement of OS and firmware security mechanisms.

关键词： bootkit hook legacy BIOS security mechanisms UEFI

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Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 103-112页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu Guobiao Jia IEEE the school of Automation Nanjing University of Science and TechnologyNanjing 210094China the school of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

关键词： Consensus distributed Nash equilibrium seeking projected gradient play quantized communication

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A multi-stream network for retrosynthesis prediction

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Frontiers of Computer Science 2024年第2期18卷 231-233页

作者： Qiang ZHANG Juan LIU Wen ZHANG Feng YANG Zhihui YANG Xiaolei ZHANG Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072China College of informatics Huazhong Agricultural UniversityWuhan 430072China

1 Introduction Retrosynthesis aims to predict a set of reactants for producing given molecules[1],which plays a significant part in the biochemistry field,such as molecular pathway design and drug *** existing methods perform better in solving this problem,most of these methods[2,3]only describe the molecules from one kind of perspective,such as they use Simplified Molecular Input Line Entry System(SMILES)strings[4]to represent the atomic components and the relationship between atoms of molecules or leverage Extended Connectivity Fingerprints(ECFPs)represent the information of the molecular sub-structure composition via binary vectors[5].

关键词： synthesis reactant hough

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Adaptive joint configuration optimization for collaborative inference in edge-cloud systems

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Science China(Information Sciences) 2024年第4期67卷 335-336页

作者： Zheming YANG Wen JI Zhi WANG Institute of computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Peng Cheng Laboratory Tsinghua Shenzhen International Graduate school Tsinghua University

With the rapid development of technologies such as deep learning and the Internet of Things(IoT), the deployment and application of various IoT devices are becoming increasingly widespread, resulting in the inference tasks generated increasing dramatically [1]. Many deep neural network(DNN) models are deployed on servers for performing inference tasks.

关键词： Inference engines

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Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

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Defence Technology（防务技术） 2024年第3期33卷 327-338页

作者： Lin Qi Yu Liu Chuanshun Gao Tao Feng Yue Yu school of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Land Surveying and Geo-informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,*** performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration *** paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint *** addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial ***,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall *** results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

关键词： Pedestrian positioning system Wearable inertial sensors Ultrasonic ranging Deep-learning Data and model dual-driven

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