Federated learning resource management for energy-constrained industrial IoT devices

Shaoshuai FAN; Jianbo WU; Hui TIAN

doi:10.11959/j.issn.1000-436x.2022126

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Federated learning resource management for energy-constrained industrial IoT devices

Papers | 更新时间：2024-06-05

- Federated learning resource management for energy-constrained industrial IoT devices
- Journal on Communications Vol. 43, Issue 8, Pages: 65-77(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2022126
  CLC： TP302
- Online First：2022-08，
  
  Published：25 August 2022
- 稿件说明：
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Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices[J]. Journal on Communications, 2022, 43(8): 65-77.
DOI：

Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices[J]. Journal on Communications, 2022, 43(8): 65-77. DOI： 10.11959/j.issn.1000-436x.2022126.

摘要

针对工业物联网联邦学习网络中由设备电池能量有限导致的设备失效、训练中断等问题，并考虑到无线资源受限的影响，提出一种动态的多维资源联合管理算法。首先，以最大化固定训练时间学习精度为目标，将优化问题解耦为相互依赖的电池能量分配子问题、设备资源分配子问题和通信资源分配子问题。其次，基于粒子群优化算法求解能耗预算下设备传输和计算资源分配策略。再次，提出资源块迭代匹配算法求解出最佳通信资源分配策略。最后，提出在线能量分配算法动态调整设备能量分配策略。仿真结果表明，与基准算法相比，所提算法能够提高模型学习精度，在能源不足场景下性能优势更明显。

Abstract

Given the impact of limited wireless resources

a dynamic multi-dimensional resource joint management algorithm was proposed

which intended to tackle the problem of device failure and training interruption caused by the limited battery energy in federated learning network in industrial Internet of things (IIoT).Firstly

the optimization problem was decoupled into battery energy allocation

equipment resource allocation and communication resource allocation sub-problems which were interdependent with the goal of maximizing the fixed-time learning accuracy.Then

the equipment transmission and computing resource allocation problem were solved based on particle swarm optimization algorithm under the given energy budget.Thereafter

the resource block iterative matching algorithm was proposed to optimize the optimal communication resource allocation strategy.Finally

the online energy allocation algorithm was proposed to adjust the energy budget allocation.Simulation results validate the proposed algorithm can improve the model learning accuracy compared with other benchmarks

and can perform better in energy shortage scenarios.

关键词

Keywords

references

LIU Y , YUAN X L , XIONG Z H , et al . Federated learning for 6G communications:challenges,methods,and future directions [J ] . China Communications , 2020 , 17 ( 9 ): 105 - 118 .

BOUZINIS P S , DIAMANTOULAKIS P D , KARAGIANNIDIS G K . Wireless federated learning (WFL) for 6G networks‐part I:research challenges and future trends [J ] . IEEE Communications Letters , 2022 , 26 ( 1 ): 3 - 7 .

ZHOU Y Q , LIU L , WANG L , et al . Service-aware 6G:an intelligent and open network based on the convergence of communication,computing and caching [J ] . Digital Communications and Networks , 2020 , 6 ( 3 ): 253 - 260 .

KONEČNÝ J , MCMAHAN H B , YU F X , et al . Federated learning:strategies for improving communication efficiency [J ] . arXiv Preprint,arXiv:1610.05492 , 2016 .

NGUYEN D C , DING M , PATHIRANA P N , et al . 6G Internet of things:a comprehensive survey [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 1 ): 359 - 383 .

GAO W F , ZHAO Z W , MIN G Y , et al . Resource allocation for latency-aware federated learning in industrial Internet of things [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 12 ): 8505 - 8513 .

IMTEAJ A , AMINI M H . FedPARL:client activity and resource-oriented lightweight federated learning model for resource-constrained heterogeneous IoT environment [J ] . Frontiers in Communications and Networks , 2021 ,2:657653.

LI T , SAHU A K , TALWALKAR A , et al . Federated learning:challenges,methods,and future directions [J ] . IEEE Signal Processing Magazine , 2020 , 37 ( 3 ): 50 - 60 .

徐海黎 , 解祥荣 , 庄健 , 等 . 工业机器人的最优时间与最优能量轨迹规划 [J ] . 机械工程学报 , 2010 , 46 ( 9 ): 19 - 25 .

XU H L , XIE X R , ZHUANG J , et al . Global time-energy optimal planning of industrial robot trajectories [J ] . Journal of Mechanical Engineering , 2010 , 46 ( 9 ): 19 - 25 .

LIN X H , ZHANG J , XIANG L , et al . Energy consumption optimization for UAV assisted private blockchain-based IIoT networks [C ] // Proceedings of 2021 IEEE 94th Vehicular Technology Conference . Piscataway:IEEE Press , 2021 : 1 - 7 .

IMTEAJ A , THAKKER U , WANG S Q , et al . A survey on federated learning for resource-constrained IoT devices [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 1 ): 1 - 24 .

MA Z G , XU Y , XU H L , et al . Adaptive batch size for federated learning in resource-constrained edge computing [J ] . IEEE Transactions on Mobile Computing,2021:doi.org/ 10.1109/TMC.2021.3075291 .

LI T , SAHU A K , ZAHEER M , et al . Federated optimization in heterogeneous networks [J ] . arXiv Preprint,arXiv:1812.06127 , 2018 .

LI L , XIONG H Y , GUO Z S , et al . SmartPC:hierarchical pace control in real-time federated learning system [C ] // Proceedings of 2019 IEEE Real-Time Systems Symposium . Piscataway:IEEE Press , 2019 : 406 - 418 .

LI L , WANG J , CHEN X , et al . Multi-layer coordination for high-performance energy-efficient federated learning [C ] // Proceedings of 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) . Piscataway:IEEE Press , 2020 : 1 - 10 .

XU J , WANG H Q . Client selection and bandwidth allocation in wireless federated learning networks:a long-term perspective [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 2 ): 1188 - 1200 .

YANG Z H , CHEN M Z , SAAD W , et al . Energy efficient federated learning over wireless communication networks [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 3 ): 1935 - 1949 .

TAK A , CHERKAOUI S . Federated edge learning:design issues and challenges [J ] . IEEE Network , 2021 , 35 ( 2 ): 252 - 258 .

DINH C T , TRAN N H , NGUYEN M N H , et al . Federated learning over wireless networks:convergence analysis and resource allocation [J ] . IEEE/ACM Transactions on Networking , 2021 , 29 ( 1 ): 398 - 409 .

YIN B S , CHEN Z Y , TAO M X . Joint user scheduling and resource allocation for federated learning over wireless networks [C ] // Proceedings of 2020 IEEE Global Communications Conference . Piscataway:IEEE Press , 2020 : 1 - 6 .

TAÏK A , MLIKA Z , CHERKAOUI S . Data-aware device scheduling for federated edge learning [J ] . IEEE Transactions on Cognitive Communications and Networking , 2022 , 8 ( 1 ): 408 - 421 .

SALEHI M , HOSSAIN E . Federated learning in unreliable and resource-constrained cellular wireless networks [J ] . IEEE Transactions on Communications , 2021 , 69 ( 8 ): 5136 - 5151 .

WANG S Q , TUOR T , SALONIDIS T , et al . Adaptive federated learning in resource constrained edge computing systems [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1205 - 1221 .

SHI W Q , ZHOU S , NIU Z S , et al . Joint device scheduling and resource allocation for latency constrained wireless federated learning [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 1 ): 453 - 467 .

ZENG Q S , DU Y Q , HUANG K B , et al . Energy-efficient radio resource allocation for federated edge learning [C ] // Proceedings of 2020 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2020 : 1 - 6 .

SONG Q , LEI S Y , SUN W , et al . Adaptive federated learning for digital twin driven industrial Internet of things [C ] // Proceedings of 2021 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2021 : 1 - 6 .

FENG C Y , WANG Y D , ZHAO Z Y , et al . Joint optimization of data sampling and user selection for federated learning in the mobile edge computing systems [C ] // Proceedings of 2020 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2020 : 1 - 6 .

REN J K , YU G D , DING G Y . Accelerating DNN training in wireless federated edge learning systems [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 219 - 232 .

HE Y H , REN J K , YU G D , et al . Importance-aware data selection and resource allocation in federated edge learning system [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 11 ): 13593 - 13605 .

LIANG Q L , DURRANI T S , LIANG J , et al . Guest editorial special issue on 6G-enabled Internet of things [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 20 ): 15037 - 15040 .

BANY S H , AL-OBIEDOLLAH H , MAHASEES R , et al . Opportunistic non-contiguous OFDMA scheduling framework for future B5G/6G cellular networks [J ] . Simulation Modelling Practice and Theory , 2022 ,119:102563.

PAN Y J , PAN C H , YANG Z H , et al . Resource allocation for D2D communications underlaying a NOMA-based cellular network [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 1 ): 130 - 133 .

XI Y , BURR A , WEI J B , et al . A general upper bound to evaluate packet error rate over quasi-static fading channels [J ] . IEEE Transactions on Wireless Communications , 2011 , 10 ( 5 ): 1373 - 1377 .

CHEN M Z , YANG Z H , SAAD W , et al . A joint learning and communications framework for federated learning over wireless networks [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 1 ): 269 - 283 .

REN J K , HE Y H , WEN D Z , et al . Scheduling for cellular federated edge learning with importance and channel awareness [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 11 ): 7690 - 7703 .

SONG Z D , SUN H G , YANG H H , et al . Reputation-based federated learning for secure wireless networks [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 2 ): 1212 - 1226 .

HASSAN R , COHANIM B , DE WECK O , et al . A comparison of particle swarm optimization and the genetic algorithm [C ] // Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics and Materials Conference . Reston:AIAA , 2005 :1897.

ANSERE J A , HAN G J , BONSU K A , et al . Energy-efficient joint power allocation and user selection algorithm for data transmission in Internet-of-things networks [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 9 ): 8736 - 8747 .

CUI H , ZHANG J J , CUI C F , et al . Solving large-scale assignment problems by Kuhn-Munkres algorithm [C ] // Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) . Paris:Atlantis Press , 2016 : 822 - 827 .

WEI K , LI J , MA C , et al . Low-latency federated learning over wireless channels with differential privacy [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 1 ): 290 - 307 .

YANG H H , LIU Z Z , QUEK T Q S , et al . Scheduling policies for federated learning in wireless networks [J ] . IEEE Transactions on Communications , 2020 , 68 ( 1 ): 317 - 333 .

CHEN M Z , POOR H V , SAAD W , et al . Convergence time minimization of federated learning over wireless networks [C ] // Proceedings of 2020 IEEE International Conference on Communications . Piscataway:IEEE Press , 2020 : 1 - 6 .

SUN Y X , ZHOU S , GÜNDÜZ D , . Energy-aware analog aggregation for federated learning with redundant data [C ] // Proceedings of 2020 IEEE International Conference on Communications . Piscataway:IEEE Press , 2020 : 1 - 7 .

NGUYEN H T , SEHWAG V , HOSSEINALIPOUR S , et al . Fast-convergent federated learning [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 201 - 218 .

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