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.

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.

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.

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.

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.

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.

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.

XIAO Y, SHI G M, KRUNZ M ,et al. Towards ubiquitous AI in 6G with federated learning[EB]. 2020.

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.

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.

PATIL A, IYER S, PANDYA R J . A survey of machine learning algorithms for 6G wireless networks[J]. arXiv preprint, 2022,arXiv:2203.08429.

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.

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.

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.

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.

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.

ABREHA H G, HAYAJNEH M, SERHANI M A . Federated learning in edge computing:a systematic survey[J]. Sensors (Basel,Switzerland), 2022,22(2): 450.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

ZHUO H K, FENG W F, XU Q ,et al. Federated reinforcement learning[EB]. 2019.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

RIBERO M, VIKALO H . Communication-efficient federated learning via optimal client sampling[J]. arXiv preprint, 2020,arXiv:2007.15197.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

PARK J, LIM H . Privacy-preserving federated learning using homomorphic encryption[J]. Applied Sciences, 2022,12(2): 734.

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.

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.

PEJIC I, WANG R, LIANG K ,et al. Effect of homomorphic encryption on the performance of training federated learning generative adversarial networks[EB]. 2022.

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.

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.

LI T, SANJABI M, BEIRAMI A ,et al. Fair resource allocation in federated learning[J]. arXiv preprint, 2019,arXiv:1905.10497.

CHEN H Y, CHAO W L . FedBE:making Bayesian model ensemble applicable to federated learning[J]. arXiv preprint, 2020,arXiv:2009.01974.

SHAMSIAN A, NAVON A, FETAYA E ,et al. Personalized federated learning using hypernetworks[J]. arXiv preprint, 2021,arXiv:2103.04628.

ARIVAZHAGAN M G, AGGARWAL V, SINGH A K ,et al. Federated learning with personalization layers[J]. arXiv preprint, 2019,arXiv:1912.00818.

COLLINS L, HASSANI H, MOKHTARI A ,et al. Exploiting shared representations for personalized federated learning[J]. arXiv preprint, 2021,arXiv:2102.07078.

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.

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.

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.

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.

DANDI Y, BARBA L, JAGGI M . Implicit gradient alignment in distributed and federated learning[J]. arXiv preprint, 2021,arXiv:2106.13897.

RUAN Y C, JOE-WONG C, . FedSoft:soft clustered federated learning with proximal local updating[J]. arXiv preprint, 2021,arXiv:2112.06053.

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.

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.

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.

LI T, HU S Y, BEIRAMI A ,et al. Ditto:fair and robust federated learning through personalization[J]. arXiv preprint, 2020,arXiv:2012.04221.

FINN C, ABBEEL P, LEVINE S . Model-agnostic meta-learning for fast adaptation of deep networks[J]. arXiv preprint, 2017,arXiv:1703.03400.

JIANG Y H, KONEČNÝ J, RUSH K ,et al. Improving federated learning personalization via model agnostic meta learning[J]. arXiv preprint, 2019,arXiv:1909.12488.

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.

HINTON G, VINYALS O, DEAN J . Distilling the knowledge in a neural network[J]. arXiv preprint, 2015,arXiv:1503.02531.

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.

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.

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.

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.

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.

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.

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.

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.

ZHANG H N, WANG F . Wireless sensor energy harvesting and management[EB]. 2022.

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.

PANG L, YANG C G, CHEN D Y ,et al. A survey on intent-driven networks[J]. IEEE Access, 2020,8: 22862-22873.

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.

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.

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.

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.