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

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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 networks.To 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 traffic.Recently,federated learning(FL)has demonstrated its empirical success in edge computing due to its privacy-preserving advantages.Thus,it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks,which are the major workloads in MEC.Unfortunately,EDs are typically powered by batteries with limited capacity,which brings challenges when performing energy-intensive FL tasks.To address these challenges,many strategies have been proposed to save energy in FL.Considering 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 MEC.Specifically,we first introduce the system model and energy consumption models in FL,in terms of computation and communication.Then we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives:learning-based,resource allocation,and client selection.We conduct a detailed analysis of these strategies,comparing their advantages and disadvantages.Additionally,we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental results.Finally,several potential future research directions for energy-efficient FL are discussed.

关键词： mobile edge computing Federated learning Energy-efficient

Air-Ground Collaborative mobile edge computing:Architecture,Challenges,and Opportunities

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China Communications 2024年第5期21卷 1-16页

作者： Qin Zhen He Shoushuai Wang Hai Qu Yuben Dai Haipeng Xiong Fei Wei Zhenhua Li Hailong College of Communications Engineering Army Engineering University of PLANanjing 210000China Department of Information and Communication Noncommissioned Officer Academy of PAPHangzhou 310000China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China CMC Political and Law Commission Beijing100120China Xi’an Research Institute of High Technology Xi’an 710025China

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

关键词： air-ground architecture collaborative mobile edge computing

computing Resource Allocation for Blockchain-Based mobile edge computing

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 863-885页

作者： Wanbo Zhang Yuqi Fan Jun Zhang Xu Ding Jung Yoon Kim School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui ProvinceHefei University of TechnologyHefei230601China Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei University of TechnologyHefei230009China College of Future Industry Gachon UniversitySeongnam13120South Korea

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： mobile edge computing blockchain resource allocation

Online Learning-Based Offloading Decision and Resource Allocation in mobile edge computing-Enabled Satellite-Terrestrial Networks

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China Communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted mobile edge computing

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China Communications 2024年第4期21卷 88-103页

作者： Ke Hongchang Wang Hui Sun Hongbin Halvin Yang School of Computer Technology and Engineering Changchun Institute of TechnologyChangchun 130012China College of Computer Science and Engineering Changchun University of TechnologyChangchun 130012China Department of Electronic and Electrical Engineering University College LondonLondonUK

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

关键词： deep reinforcement learning mobile edge computing service caching service migrating

Multiuser Computation Offloading for Long-Term Sequential Tasks in mobile edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network edge.However,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term objectives.mobile applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential tasks.In this study,we first proposed a novel multiuser computation offloading framework for long-term sequential tasks.Then,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading problem.Moreover,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve them.We further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

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Digital Communications and Networks 2022年第6期8卷 1048-1058页

作者： Michael Pendo John Mahenge Chunlin Li Camilius A.Sanga Department of Informatics and Information Technology Sokoine University of AgricultureMorogoro3038Tanzania School of Computer Science and Technology Wuhan University of TechnologyWuhanHubei430063China

mobile edge computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as mobile Cloud computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

关键词： mobile edge computing Quality of experience Task ofloading Communication networks Particle swarm optimizati on

Users'experience matter:Delay sensitivity-aware computation offloading in mobile edge computing

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Digital Communications and Networks 2022年第6期8卷 955-963页

作者： Mingzhi Wang Tao Wu Tao Ma Xiaochen Fan Mingxing Ke National University of Defense Technology Hefei230031China Consulting Center for Strategic Assessment Academy of Military ScienceBeijing100091China

As a promising computing paradigm,mobile edge computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)systems.Most existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied users.In this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints.Specifically,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories.Then,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC system.By starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges.Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

关键词： mobile edge computing Computation floading Delay sensitivity Centralized ofloading

Task offloading mechanism based on federated reinforcement learning in mobile edge computing

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Digital Communications and Networks 2023年第2期9卷 492-504页

作者： Jie Li Zhiping Yang Xingwei Wang Yichao Xia Shijian Ni School of Computer Science and Engineering Northeastern UniversityShenyang110000China

With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.mobile edge computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

关键词： mobile edge computing Task offloading QoS Deep reinforcement learning Federated learning