联邦学习赋能6G网络综述

doi:10.11959/j.issn.2096-3750.2023.00323

Abstract

Abstract:

It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence (AI).Thanks to the distinct machine learning architecture of data processing locally, federated learning (FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G.Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things (IoT) scenarios was analyzed.And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated.Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.

Key words: 6G networks, internet of things, artificial intelligence, federated learning

CLC Number:

TP393
TN92

Guanglei GENG, Bo GAO, Ke XIONG, Pingyi FAN, Yang LU, Yuwei WANG. A survey of federated learning for 6G networks[J]. Chinese Journal on Internet of Things, 2023, 7(2): 50-66.

Figures/Tables 5

References 113

[1]	栾宁, 熊轲, 张煜 ,等. 6G:典型应用、关键技术与面临挑战[J]. 物联网学报, 2022,6(1): 29-43.
	LUAN N , XIONG K , ZHANG Y ,et al. 6G:typical applications,key technologies and challenges[J]. Chinese Journal on Internet of Things, 2022,6(1): 29-43.
[2]	SHAHRAKI A , ABBASI M , PIRAN M J ,et al. A comprehensive survey on 6G networks:applications,core services,enabling technologies,and future challenges[EB]. 2021.
[3]	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.
[4]	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.
[5]	YANG Z H , CHEN M Z , WONG K K ,et al. Federated learning for 6G:applications,challenges,and opportunities[J]. Engineering, 2022,8: 33-41.
[6]	AL-QURAAN M , MOHJAZI L , BARIAH L ,et al. Edge-native intelligence for 6G communications driven by federated learning:a survey of trends and challenges[EB]. 2021.
[7]	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.
[8]	XIAO Y , SHI G M , KRUNZ M ,et al. Towards ubiquitous AI in 6G with federated learning[EB]. 2020.
[9]	NGUYEN D C , DING M , PATHIRANA P N ,et al. Federated learning for internet of things:a comprehensive survey[J]. IEEE Communications Surveys ＆ Tutorials, 2021,23(3): 1622-1658.
[10]	白卫岗, 盛敏, 杜盼盼 . 6G卫星物联网移动性管理：挑战与关键技术[J]. 物联网学报, 2020,4(1): 104-110.
	BAI W G , SHENG M , DU P P . Mobility management of the 6G satellite IoT:challenges and key techniques[J]. Chinese Journal on Internet of Things, 2020,4(1): 104-110.
[11]	PATIL A , IYER S , PANDYA R J . A survey of machine learning algorithms for 6G wireless networks[J]. arXiv preprint, 2022,arXiv:2203.08429.
[12]	中国通信学会. 通感算一体化网络前沿报告[R]. 2021.
	China Institute of Communications. Synesthesia integrated network frontier report[R]. 2021.
[13]	GUO H Z , ZHOU X Y , LIU J J ,et al. Vehicular intelligence in 6G:networking,communications,and computing[J]. Vehicular Communications, 2022,33:100399.
[14]	LIU T , DING J H , WANG T ,et al. Towards fast and accurate federated learning with non-IID data for cloud-based IoT applications[J]. arXiv preprint, 2022,arXiv:2201.12515.
[15]	LIU G Y , ZHU J K , HUANG Y M ,et al. Guest editorial:6G towards 2030:from key technology to network architecture[J]. China Communications, 2022,19(3): 3-6.
[16]	HOKAZONO Y , KOHARA H , KISHIYAMA Y ,et al. Extreme coverage extension in 6G:cooperative non-terrestrial network architecture integrating terrestrial networks[C]// Proceedings of 2022 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2022: 138-143.
[17]	ABREHA H G , HAYAJNEH M , SERHANI M A . Federated learning in edge computing:a systematic survey[J]. Sensors (Basel,Switzerland), 2022,22(2): 450.
[18]	ZHAO B R , CUI Q M , LIANG S Y ,et al. Green concerns in federated learning over 6G[J]. China Communications, 2022,19(3): 50-69.
[19]	ABAD M S H , OZFATURA E , GUNDUZ D ,et al. Hierarchical federated learning ACROSS heterogeneous cellular networks[C]// Proceedings of ICASSP 2020 - 2020 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP). Piscataway:IEEE Press, 2020: 8866-8870.
[20]	LIU L M , ZHANG J , SONG S H ,et al. Client-edge-cloud hierarchical federated learning[C]// Proceedings of ICC 2020 - 2020 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2020: 1-6.
[21]	HOSSEINALIPOUR S , BRINTON C G , AGGARWAL V ,et al. From federated to fog learning:distributed machine learning over heterogeneous wireless networks[J]. IEEE Communications Magazine, 2020,58(12): 41-47.
[22]	CHEN M Z , POOR H V , SAAD W ,et al. Wireless communications for collaborative federated learning[J]. IEEE Communications Magazine, 2020,58(12): 48-54.
[23]	NISHIO T , YONETANI R . Client selection for federated learning with heterogeneous resources in mobile edge[C]// Proceedings of ICC 2019 2019 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2019: 1-7.
[24]	LIU X L , DENG Y S , MAHMOODI T . A novel hybrid split and federated learning architecture in wireless UAV networks[C]// Proceedings of ICC 2022 - IEEE International Conference on Communications. Piscataway:IEEE Press, 2022: 1-6.
[25]	PRATHIBA S B , RAJA G , ANBALAGAN S ,et al. Cyber twin-driven federated learning based personalized service provision for 6G-V2X[J]. IEEE Transactions on Vehicular Technology, 2022,71(5): 4632-4641.
[26]	BANO S , TONELLOTTO N , CASSARà P ,et al. KafkaFed:two-tier federated learning communication architecture for Internet of vehicles[C]// Proceedings of 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). Piscataway:IEEE Press, 2022: 515-520.
[27]	WANG Z , LI X H , WU T H ,et al. A credibility-aware swarm-federated deep learning framework in Internet of vehicles[J]. arXiv preprint, 2021,arXiv:2108.03981.
[28]	ZHOU X K , LIANG W , SHE J H ,et al. Two-layer federated learning with heterogeneous model aggregation for 6G supported Internet of vehicles[J]. IEEE Transactions on Vehicular Technology, 2021,70(6): 5308-5317.
[29]	PENG Y Q , CHEN Z Y , CHEN Z X ,et al. BFLP:an adaptive federated learning framework for Internet of vehicles[J]. Mobile Information Systems, 2021: 1-18.
[30]	CHAI H Y , LENG S P , CHEN Y J ,et al. A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in Internet of vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021,22(7): 3975-3986.
[31]	QU Y B , DONG C , ZHENG J C ,et al. Empowering the edge intelligence by air-ground integrated federated learning in 6G networks[J]. arXiv preprint, 2020,arXiv:2007.13054.
[32]	KHAN L U , TUN Y K , ALSENWI M ,et al. A dispersed federated learning framework for 6G-enabled autonomous driving cars[J]. arXiv preprint, 2021,arXiv:2105.09641.
[33]	LIU S , YU J , DENG X H ,et al. FedCPF:an efficient-communication federated learning approach for vehicular edge computing in 6G communication networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022,23(2): 1616-1629.
[34]	YU A , YANG Q K , DOU L H ,et al. Federated imitation learning:a cross-domain knowledge sharing framework for traffic scheduling in 6G ubiquitous IoT[J]. IEEE Network, 2021,35(5): 136-142.
[35]	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.
[36]	LI J , GAO H , LV T J ,et al. Deep reinforcement learning based computation offloading and resource allocation for MEC[C]// Proceedings of 2018 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2018: 1-6.
[37]	PENG H X , SHEN X M . Multi-agent reinforcement learning based resource management in MEC- and UAV-assisted vehicular networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(1): 131-141.
[38]	ZARIF A H , AZMI P , MOKARI N ,et al. AoI minimization in energy harvesting and spectrum sharing enabled 6G networks[J]. arXiv preprint, 2021,arXiv:2107.00340.
[39]	TANG S P , ZHOU W Q , CHEN L Y ,et al. Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks[J]. Physical Communication, 2021,47:101381.
[40]	YE Y T , WEI W S , GENG D Q ,et al. Dynamic coordination in UAV swarm assisted MEC via decentralized deep reinforcement learning[C]// Proceedings of 2020 International Conference on Wireless Communications and Signal Processing (WCSP). Piscataway:IEEE Press, 2020: 1064-1069.
[41]	WEI D W , MA J F , LUO L B ,et al. Computation offloading over multi-UAV MEC network:a distributed deep reinforcement learning approach[J]. Computer Networks, 2021,199:108439.
[42]	YANG Z , LIU Y W , CHEN Y . Distributed reinforcement learning for NOMA-enabled mobile edge computing[C]// Proceedings of 2020 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway:IEEE Press, 2020: 1-6.
[43]	TEHRANI P , RESTUCCIA F , LEVORATO M . Federated deep reinforcement learning for the distributed control of NextG wireless networks[C]// Proceedings of 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). Piscataway:IEEE Press, 2022: 248-253.
[44]	ZHUO H K , FENG W F , XU Q ,et al. Federated reinforcement learning[EB]. 2019.
[45]	MESSAOUD S , BRADAI A , AHMED O B ,et al. Deep federated Q-learning-based network slicing for industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2021,17(8): 5572-5582.
[46]	NIE Y W , ZHAO J H , GAO F F ,et al. Semi-distributed resource management in UAV-aided MEC systems:a multi-agent federated reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2021,70(12): 13162-13173.
[47]	ZANG L Q , ZHANG X , GUO B R . Federated deep reinforcement learning for online task offloading and resource allocation in WPC-MEC networks[J]. IEEE Access, 2022,10: 9856-9867.
[48]	XUE Z Y , ZHOU P , XU Z C ,et al. A resource-constrained and privacy-preserving edge-computing-enabled clinical decision system:a federated reinforcement learning approach[J]. IEEE Internet of Things Journal, 2021,8(11): 9122-9138.
[49]	CHEN M Z , SHLEZINGER N , POOR H V ,et al. Communication-efficient federated learning[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021,118(17): e2024789118.
[50]	CHEN M Z , GüNDüZ D , HUANG K B ,et al. Distributed learning in wireless networks:recent progress and future challenges[J]. IEEE Journal on Selected Areas in Communications, 2021,39(12): 3579-3605.
[51]	HUANG T S , LIN W W , SHEN L ,et al. Stochastic client selection for federated learning with volatile clients[J]. IEEE Internet of Things Journal, 2022,9(20): 20055-20070.
[52]	ZHU Z Q , WAN S , FAN P Y ,et al. Federated multiagent actor-critic learning for age sensitive mobile-edge computing[J]. IEEE Internet of Things Journal, 2022,9(2): 1053-1067.
[53]	LI R J , XU H B , ZHANG G P ,et al. Latency optimization for federated learning over wireless power transfer[J]. Physical Communication, 2022,53:101765.
[54]	HUANG N , WANG T S , WU Y ,et al. Delay minimization for intelligent reflecting surface assisted federated learning[J]. China Communications, 2022,19(4): 216-229.
[55]	PRATHIBA S B , RAJA G , ANBALAGAN S ,et al. Federated learning empowered computation offloading and resource management in 6G-V2X[J]. IEEE Transactions on Network Science and Engineering, 2022,9(5): 3234-3243.
[56]	YANG Q , LIU Y , CHEN T J ,et al. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019,10(2): 1-19.
[57]	RIBERO M , VIKALO H . Communication-efficient federated learning via optimal client sampling[J]. arXiv preprint, 2020,arXiv:2007.15197.
[58]	LI X Y , QU Z , TANG B ,et al. Stragglers are not disaster:a hybrid federated learning algorithm with delayed gradients[J]. arXiv preprint, 2021,arXiv:2102.06329.
[59]	CHO Y J , WANG J Y , JOSHI G . Client selection in federated learning:convergence analysis and power-of-choice selection strategies[J]. arXiv preprint, 2020,arXiv:2010.01243.
[60]	YOSHIDA N , NISHIO T , MORIKURA M ,et al. MAB-based client selection for federated learning with uncertain resources in mobile networks[C]// Proceedings of 2020 IEEE Globecom Workshops (GC Wkshps. Piscataway:IEEE Press, 2021: 1-6.
[61]	WEI K , LI J , DING M ,et al. Federated learning with differential privacy:algorithms and performance analysis[J]. IEEE Transactions on Information Forensics and Security, 2020,15: 3454-3469.
[62]	MA C , LI J , DING M ,et al. On safeguarding privacy and security in the framework of federated learning[J]. IEEE Network, 2020,34(4): 242-248.
[63]	WANG R J , LAI J S , ZHANG Z Y ,et al. Privacy-preserving federated learning for Internet of medical things under edge computing[J]. IEEE Journal of Biomedical and Health Informatics, 2022.
[64]	SONG J C , WANG W Z , GADEKALLU T R ,et al. EPPDA:an efficient privacy-preserving data aggregation federated learning scheme[J]. IEEE Transactions on Network Science and Engineering, 2022,PP(99): 1.
[65]	KANAGAVELU R , LI Z X , SAMSUDIN J ,et al. Two-phase multi-party computation enabled privacy-preserving federated learning[C]// Proceedings of 2020 20th IEEE/ACM International Symposium on Cluster,Cloud and Internet Computing (CCGRID). Piscataway:IEEE Press, 2020: 410-419.
[66]	ZHU H F , MONG GOH R S , NG W K . Privacy-preserving weighted federated learning within the secret sharing framework[J]. IEEE Access, 2020,8: 198275-198284.
[67]	KANAGAVELU R , WEI Q S , LI Z X ,et al. CE-Fed:communication efficient multi-party computation enabled federated learning[J]. Array, 2022,15:100207.
[68]	MENG Q , ZHOU F , REN H N ,et al. Improving federated learning face recognition via privacy-agnostic clusters[J]. arXiv preprint, 2022,arXiv:2201.12467.
[69]	WAN Y C , QU Y Y , GAO L X ,et al. Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing[J]. Computer Networks, 2022,204:108671.
[70]	LIU W Y , CHENG J H , WANG X L ,et al. Hybrid differential privacy based federated learning for Internet of Things[J]. Journal of Systems Architecture, 2022,124:102418.
[71]	HU R , GONG Y M , GUO Y X . Federated learning with sparsified model perturbation:improving accuracy under client-level differential privacy[J]. arXiv preprint, 2022,arXiv:2202.07178.
[72]	WU X , ZHANG Y T , SHI M Y ,et al. An adaptive federated learning scheme with differential privacy preserving[J]. Future Generation Computer Systems, 2022,127: 362-372.
[73]	HUANG J , XU C , JI Z H ,et al. AFLPC:an asynchronous federated learning privacy-preserving computing model applied to 5G-V2X[J]. Security and Communication Networks, 2022,2022: 1-11.
[74]	PARK J , LIM H . Privacy-preserving federated learning using homomorphic encryption[J]. Applied Sciences, 2022,12(2): 734.
[75]	KU H C , SUSILO W , ZHANG Y D ,et al. Privacy-preserving federated learning in medical diagnosis with homomorphic re-Encryption[J]. Computer Standards ＆ Interfaces, 2022,80:103583.
[76]	WIBAWA F , CATAK F O , SARP S ,et al. Homomorphic encryption and federated learning based privacy-preserving CNN training:COVID-19 detection use-case[J]. arXiv preprint, 2022,arXiv:2204.07752.
[77]	PEJIC I , WANG R , LIANG K ,et al. Effect of homomorphic encryption on the performance of training federated learning generative adversarial networks[EB]. 2022.
[78]	ONOSZKO N , KARLSSON G , MOGREN O ,et al. Decentralized federated learning of deep neural networks on non-iid data[J]. arXiv preprint, 2021,arXiv:2107.08517.
[79]	HUANG Y T , CHU L Y , ZHOU Z R ,et al. Personalized cross-silo federated learning on non-IID data[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021,35(9): 7865-7873.
[80]	LI T , SANJABI M , BEIRAMI A ,et al. Fair resource allocation in federated learning[J]. arXiv preprint, 2019,arXiv:1905.10497.
[81]	CHEN H Y , CHAO W L . FedBE:making Bayesian model ensemble applicable to federated learning[J]. arXiv preprint, 2020,arXiv:2009.01974.
[82]	SHAMSIAN A , NAVON A , FETAYA E ,et al. Personalized federated learning using hypernetworks[J]. arXiv preprint, 2021,arXiv:2103.04628.
[83]	ARIVAZHAGAN M G , AGGARWAL V , SINGH A K ,et al. Federated learning with personalization layers[J]. arXiv preprint, 2019,arXiv:1912.00818.
[84]	COLLINS L , HASSANI H , MOKHTARI A ,et al. Exploiting shared representations for personalized federated learning[J]. arXiv preprint, 2021,arXiv:2102.07078.
[85]	SATTLER F , MULLER K R , SAMEK W . Clustered federated learning:model-agnostic distributed multitask optimization under privacy constraints[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021,32(8): 3710-3722.
[86]	LI XX , JIANG M R , ZHANG X F ,et al. FedBN:federated learning on non-IID features via local batch normalization[J]. arXiv preprint, 2021,arXiv:2102.07623.
[87]	DINH C T , TRAN N H , NGUYENT D . Personalized federated learning with Moreau envelopes[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. New York:ACM Press, 2020: 21394-21405.
[88]	ACHITUVE I , SHAMSIAN A , NAVON A ,et al. Relational learning with Gaussian processes[M]. Advances in Neural Information Processing Systems 19. Vancouver: The MIT Press, 2007.
[89]	DANDI Y , BARBA L , JAGGI M . Implicit gradient alignment in distributed and federated learning[J]. arXiv preprint, 2021,arXiv:2106.13897.
[90]	RUAN Y C , JOE-WONG C , . FedSoft:soft clustered federated learning with proximal local updating[J]. arXiv preprint, 2021,arXiv:2112.06053.
[91]	YANG M , WANG X M , QIAN H ,et al. An improved federated learning algorithm for privacy preserving in cybertwin-driven 6G system[J]. IEEE Transactions on Industrial Informatics, 2022,18(10): 6733-6742.
[92]	AAMER B , CHERGUI H , BENJILLALI M ,et al. Entropy-driven stochastic federated learning in non-IID 6G edge-RAN[J]. Frontiers in Communications and Networks, 2021,2:739414.
[93]	SMITH V , CHIANG C K , SANJABI M ,et al. Federated multi-task learning[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:ACM Press, 2017: 4427-4437.
[94]	LI T , HU S Y , BEIRAMI A ,et al. Ditto:fair and robust federated learning through personalization[J]. arXiv preprint, 2020,arXiv:2012.04221.
[95]	FINN C , ABBEEL P , LEVINE S . Model-agnostic meta-learning for fast adaptation of deep networks[J]. arXiv preprint, 2017,arXiv:1703.03400.
[96]	JIANG Y H , KONE?NY J , RUSH K ,et al. Improving federated learning personalization via model agnostic meta learning[J]. arXiv preprint, 2019,arXiv:1909.12488.
[97]	ACAR D A E , ZHAO Y , ZHU R ,et al. Debiasing model updates for improving personalized federated training[C]// International Conference on Machine Learning. New York:PMLR, 2021: 21-31.
[98]	HINTON G , VINYALS O , DEAN J . Distilling the knowledge in a neural network[J]. arXiv preprint, 2015,arXiv:1503.02531.
[99]	ZHU Z D , HONG J Y , ZHOU J Y . Data-free knowledge distillation for heterogeneous federated learning[J]. Proceedings of Machine Learning Research, 2021,139: 12878-12889.
[100]	LIN T , KONG L J , STICH S U ,et al. Ensemble distillation for robust model fusion in federated learning[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. New York:ACM Press, 2020: 2351-2363.
[101]	KAUL S , GRUTESER M , RAI V ,et al. Minimizing age of information in vehicular networks[C]// Proceedings of 2011 8th Annual IEEE Communications Society Conference on Sensor,Mesh and Ad Hoc Communications and Networks. Piscataway:IEEE Press, 2011: 350-358.
[102]	熊轲, 胡慧敏, 艾渤 ,等. 6G时代信息新鲜度优先的无线网络设计[J]. 物联网学报, 2020,4(1): 80-91.
	XIONG K , HU H M , AI B ,et al. Information freshness orientated wireless network design for 6G[J]. Chinese Journal on Internet of Things, 2020,4(1): 80-91.
[103]	KHAN L U , SAAD W , HAN Z ,et al. Federated learning for internet of things:recent advances,taxonomy,and open challenges[J]. IEEE Communications Surveys ＆ Tutorials, 2021,23(3): 1759-1799.
[104]	REN H C , DENG J J , XIE X H . GRNN:generative regression neural network—a data leakage attack for federated learning[J]. ACM Transactions on Intelligent Systems and Technology, 2022,13(4): 1-24.
[105]	NGUYEN D C , DING M , PHAM Q V ,et al. Federated learning meets blockchain in edge computing:opportunities and challenges[J]. IEEE Internet of Things Journal, 2021,8(16): 12806-12825.
[106]	STRINATI E C , BELOT D , FALEMPIN A ,et al. Toward 6G:from new hardware design to wireless semantic and goal-oriented communication paradigms[C]// Proceedings of ESSCIRC 2021 - IEEE 47th European Solid State Circuits Conference (ESSCIRC). Piscataway:IEEE Press, 2021: 275-282.
[107]	ZHANG H N , WANG F . Wireless sensor energy harvesting and management[EB]. 2022.
[108]	ZHU X M , JIANG C X , KUANG L L ,et al. Cooperative transmission in integrated terrestrial-satellite networks[J]. IEEE Network, 2019,33(3): 204-210.
[109]	PANG L , YANG C G , CHEN D Y ,et al. A survey on intent-driven networks[J]. IEEE Access, 2020,8: 22862-22873.
[110]	周洋程, 闫实, 彭木根 . 意图驱动的 6G 无线接入网络[J]. 物联网学报, 2020,4(1): 72-79.
	ZHOU Y C , YAN S , PENG M G . Intent-driven 6G radio access network[J]. Chinese Journal on Internet of Things, 2020,4(1): 72-79.
[111]	VU T T , NGO D T , TRAN N H ,et al. Cell-free massive MIMO for wireless federated learning[J]. IEEE Transactions on Wireless Communications, 2020,19(10): 6377-6392.
[112]	NI W L , LIU Y W , YANG Z H ,et al. Integrating over-the-air federated learning and non-orthogonal multiple access:what role can RIS play?[J]. IEEE Transactions on Wireless Communications, 2022,21(12): 10083-10099.
[113]	JAMES SINGH K , HUANG Y M , AHMED T ,et al. Micro-LED as a promising candidate for high-speed visible light communication[J]. Applied Sciences, 2020,10(20): 7384.

Metrics

Recommended 0

No Suggested Reading articles found!

FL赋能6G	概述	分类
促进灵活架构设计	FL针对6G移动边缘场景灵活设计分布式网络架构	传统网络场景架构改进^[18-24]
		车联网场景架构设计^[25-30]
促进高效资源利用	FL结合决策算法优化6G网络中的分布式资源利用	FL结合传统优化方法^[31-35]
		FL结合DRL算法^[44-48]
促进可靠数据传输	FL在架构、算法层面，降低6G场景中的时延和通信成本	基于全局优化通信指标^[51-55]
		基于参与方优化通信指标^[56-60]
促进安全隐私保护	FL结合隐私保护技术，确保6G分布式场景模型安全传输	FL结合安全多方计算^[62-67]
		FL结合差分隐私^[68-73]
		FL结合同态加密^[74-77]
促进个性服务提供	FL克服6G数据Non-IID分布特点，提供个性化智能服务	基于服务端实现个性化FL^[79-82]
		基于客户端实现个性化FL^[83-92]
		基于其他技术实现个性化FL^[93-100]

A survey of federated learning for 6G networks

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 113

Related Articles 15

Metrics

Recommended 0

[1]	Jing WU, Sheng LI, Jing ZHANG, Ming XIN, Ruowen TAO, Zhou ZHOU, Lijia PAN, Yi SHI. New flexible sensor for the internet of things [J]. Chinese Journal on Internet of Things, 2023, 7(2): 1-14.
[2]	Kai JIANG, Yue CAO, Huan ZHOU, Xuefeng REN, Yongdong ZHU, Hai LIN. Edge intelligence empowered internet of vehicles: concept, framework, issues, implementation, and prospect [J]. Chinese Journal on Internet of Things, 2023, 7(1): 37-48.
[3]	Bin SHEN, Yinbo LI, Xiaowei LIANG. Spectrum access control for cognitive internet of things users based on enhanced weighted centroid localization [J]. Chinese Journal on Internet of Things, 2023, 7(1): 93-108.
[4]	Jiahui GUO, Zhuoyue CHEN, Wei GAO, Xijun WANG, Xinghua SUN, Lin GAO. Clients selection method based on knapsack model in federated learning [J]. Chinese Journal on Internet of Things, 2022, 6(4): 158-168.
[5]	Yingyun GUO, Bo GAO, Zhifei ZHANG, Yu ZHANG, Ke XIONG. An incentive mechanism with bandwidth allocation for federated learning [J]. Chinese Journal on Internet of Things, 2022, 6(4): 82-92.
[6]	Jun SUN, Shangweikang ZHAO. Energy-saving computation offloading scheme based on Sarsa algorithm in industrial internet of things [J]. Chinese Journal on Internet of Things, 2022, 6(3): 82-90.
[7]	Zaichen ZHANG, Xiaohu YOU, Jian DANG, Liang WU, Bingcheng ZHU, Ji CHEN, Lei WANG. Optical wireless communication and internet of things [J]. Chinese Journal on Internet of Things, 2022, 6(3): 1-13.
[8]	Nuo HUANG, Weijie LIU, Chen GONG. Industrial IoT oriented petahertz communication [J]. Chinese Journal on Internet of Things, 2022, 6(3): 37-46.
[9]	Wei WANG, Renqian GU, Li3 PENG, Jijun ZHAO, Zhongcheng WEI, Cunxi CHANG. Robust optimization of air based relay for internet of things based on UAV [J]. Chinese Journal on Internet of Things, 2022, 6(1): 101-112.
[10]	Mingjuan WU, Shuyi CHEN, Haitao LIU. Study of international standard ISO/IEC 30144: 2020 applied in intelligent substation auxiliary monitoring [J]. Chinese Journal on Internet of Things, 2022, 6(1): 123-132.
[11]	Jiahua MA, Xinghua SUN, Wenchao XIA, Xijun WANG, Hongzhou TAN, Hongbo ZHU. Node selection based on label quantity information in federated learning [J]. Chinese Journal on Internet of Things, 2021, 5(4): 46-53.
[12]	Hao JIANG, Hongming CHEN, Yilong CAO, Haoyang CUI. Comparison of MIMO based on high capacity LPWAN technology TurMass^TM and LoRa [J]. Chinese Journal on Internet of Things, 2021, 5(4): 54-61.
[13]	Zhongcheng WEI, Xinqiu ZHANG, Bin LIAN, Wei WANG, Jijun ZHAO. A survey on Wi-Fi signal based identification technology [J]. Chinese Journal on Internet of Things, 2021, 5(4): 107-119.
[14]	Yinghai XIE, Yu ZHANG. Electricity meter area identification technology based on channel coding theory [J]. Chinese Journal on Internet of Things, 2021, 5(4): 137-144.
[15]	Yiyang HU, Lina QI. Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing [J]. Chinese Journal on Internet of Things, 2021, 5(3): 78-85.