浏览全部资源
扫码关注微信
南京邮电大学通信与信息工程学院,江苏 南京 210003
[ "刘淼(1988- ),男,江苏淮安人,博士,南京邮电大学讲师、硕士生导师,主要研究方向为智能无线通信、联邦学习、车联网、工业物联网等。" ]
[ "林婉茹(2000- ),女,安徽淮北人,南京邮电大学博士生,主要研究方向为深度学习、车联网、联邦学习等。" ]
[ "王琴(1988- ),女,河南周口人,博士,南京邮电大学副研究员,主要研究方向为低空智联网、工业互联网、资源可信共享等。" ]
[ "桂冠(1982- ),男,安徽安庆人,博士,南京邮电大学教授、博士生导师,主要研究方向为人工智能、深度学习、智能通信、智能物联网等。" ]
收稿日期:2024-06-05,
修回日期:2024-09-03,
纸质出版日期:2024-10-25
移动端阅览
刘淼,林婉茹,王琴等.车联网联邦学习的数据异质性问题及基于个性化的解决方法综述[J].通信学报,2024,45(10):207-224.
LIU Miao,LIN Wanru,WANG Qin,et al.Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles[J].Journal on Communications,2024,45(10):207-224.
刘淼,林婉茹,王琴等.车联网联邦学习的数据异质性问题及基于个性化的解决方法综述[J].通信学报,2024,45(10):207-224. DOI: 10.11959/j.issn.1000-436x.2024170.
LIU Miao,LIN Wanru,WANG Qin,et al.Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles[J].Journal on Communications,2024,45(10):207-224. DOI: 10.11959/j.issn.1000-436x.2024170.
在车联网(IoV)场景中,不同设备存在海量非独立同分布的数据,容易引发数据异质性问题,影响模型训练性能并威胁交通安全,对此聚焦于车联网联邦学习(FL)的数据异质性问题,通过对问题归因溯源提出了基于个性化的解决方法体系与研究新思路。首先,论述了联邦学习用于车联网的必要性,调研总结了车联网联邦学习中典型的数据异质性问题;其次,从感知、计算和传输3个环节对车联网联邦学习的数据异质性问题进行了分类和追踪;再次,引入个性化方法作为解决各类车联网联邦学习数据异质性问题的核心手段,并分析了现有个性化联邦学习的优点与不足;最后,讨论了个性化联邦学习在车联网场景中面临的研究挑战,并结合无线通信等相关技术展望了未来研究方向。
In Internet of vehicles (IoV) scenario
there was a massive amount of non-independent and identically distributed data among devices
leading to data heterogeneity problems of federated learning (FL). This problem affected the performances of model training and might pose threats to traffic safety. Therefore
the focus lied on the data heterogeneity problem of FL in IoV
the personalized solution system and new research ideas were proposed through problem attribution. Firstly
the necessity of applying FL to IoV was discussed. Through an examination of current applications
identified the data heterogeneity problems of FL in IoV. Secondly
classified and traced the data heterogeneity problems of FL in IoV
from the perspective of perception
computation
and transmission respectively. Thirdly
personalized methods were introduced as the core approaches to address the data heterogeneity problems of FL in IoV
and analyzed the advantages and disadvantages of existing personalized federated learning (PFL). Finally
the challenges encountered by PFL in IoV were outlined
along with the future research prospection related to advanced technologies on wireless communications.
陈山枝 , 葛雨明 , 时岩 . 蜂窝车联网(C-V2X)技术发展、应用及展望 [J ] . 电信科学 , 2022 , 38 ( 1 ): 1 - 12 .
CHEN S Z , GE Y M , SHI Y . Technology development, application and prospect of cellular vehicle-to-everything (C-V2X) [J ] . Telecommunications Science , 2022 , 38 ( 1 ): 1 - 12 .
中国信息通信研究院 . 车联网白皮书 [R ] . 2021 .
China Academy of Information and Communications Technology . White paper on connected vehicles [R ] . 2021 .
ABDULRAHMAN S , TOUT H , OULD-SLIMANE H , et al . A survey on federated learning: the journey from centralized to distributed on-site learning and beyond [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 7 ): 5476 - 5497 .
MAO Y Y , YOU C S , ZHANG J , et al . A survey on mobile edge computing: the communication perspective [J ] . IEEE Communications Surveys & Tutorials , 2017 , 19 ( 4 ): 2322 - 2358 .
SONG R , ZHOU L G , LAKSHMINARASIMHAN V , et al . Federated learning framework coping with hierarchical heterogeneity in cooperative ITS [C ] // Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . Piscataway : IEEE Press , 2022 : 3502 - 3508 .
谢雨良 , 田雨晴 , 张朝阳 . 面向智能通信和计算的移动边缘分布式学习:现状、挑战与方法 [J ] . 移动通信 , 2023 , 47 ( 6 ): 48 - 55 .
XIE Y L , TIAN Y Q , ZHANG C Y . Mobile edge distributed learning for intelligent communication and computing: methods, challenges and opportunities [J ] . Mobile Communications , 2023 , 47 ( 6 ): 48 - 55 .
ZHU H Y , XU J J , LIU S Q , et al . Federated learning on non-IID data: a survey [J ] . Neurocomputing , 2021 , 465 : 371 - 390 .
MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [J ] . Proceedings of Machine Learning Research , 2017 , 54 : 1273 - 1282 .
LIU Y , YU J J Q , KANG J W , et al . Privacy-preserving traffic flow prediction: a federated learning approach [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 8 ): 7751 - 7763 .
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 .
莫梓嘉 , 高志鹏 , 杨杨 , 等 . 面向车联网数据隐私保护的高效分布式模型共享策略 [J ] . 通信学报 , 2022 , 43 ( 4 ): 83 - 94 .
MO Z J , GAO Z P , YANG Y , et al . Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles [J ] . Journal on Communications , 2022 , 43 ( 4 ): 83 - 94 .
LI Q B , DIAO Y Q , CHEN Q , et al . Federated learning on non-IID data silos: an experimental study [C ] // Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2022 : 965 - 978 .
GAO D S , YAO X , YANG Q . A survey on heterogeneous federated learning [J ] . arXiv Preprint , arXiv: 2210.04505 , 2022 .
WANG R , LI H J , LIU E W . Blockchain-based federated learning in mobile edge networks with application in Internet of vehicles [J ] . arXiv Preprint , arXiv: 2103.01116 , 2021 .
LU Y L , HUANG X H , DAI Y Y , et al . Differentially private asynchronous federated learning for mobile edge computing in urban informatics [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 3 ): 2134 - 2143 .
LI Y J , TAO X F , ZHANG X F , et al . Privacy-preserved federated learning for autonomous driving [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 7 ): 8423 - 8434 .
DOOMRA S , KOHLI N , ATHAVALE S . Turn signal prediction: a federated learning case study [J ] . arXiv Preprint , arXiv: 2012.12401 , 2020 .
LIM W Y B , HUANG J Q , XIONG Z H , et al . Towards federated learning in UAV-enabled Internet of vehicles: a multi-dimensional contract-matching approach [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 8 ): 5140 - 5154 .
ZENG T C , GUO J L , KIM K J , et al . Multi-task federated learning for traffic prediction and its application to route planning [C ] // Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2021 : 451 - 457 .
ZHANG Z W , WANG H J , FAN Z P , et al . GOF-TTE: generative online federated learning framework for travel time estimation [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 23 ): 24107 - 24121 .
LI X H , CHENG L X , SUN C , et al . Federated-learning-empowered collaborative data sharing for vehicular edge networks [J ] . IEEE Network , 2021 , 35 ( 3 ): 116 - 124 .
WANG X H , ZHENG X K , LIANG X . Charging station recommendation for electric vehicle based on federated learning [J ] . Journal of Physics: Conference Series , 2021 , 1792 ( 1 ): 012055 .
SAPUTRA Y M , HOANG D T , NGUYEN D N , et al . Energy demand prediction with federated learning for electric vehicle networks [C ] // Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2019 : 1 - 6 .
ALIYU I , ENGELENBURG S V , MU’AZU M B , et al . Statistical detection of adversarial examples in blockchain-based federated forest in-vehicle network intrusion detection systems [J ] . IEEE Access , 2022 , 10 : 109366 - 109384 .
TAN A Z , YU H , CUI L Z , et al . Towards personalized federated learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2023 , 34 ( 12 ): 9587 - 9603 .
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 .
LIANG Y Y , ZHANG S , WANG Y H . Data-driven road side unit location optimization for connected-autonomous-vehicle-based intersection control [J ] . Transportation Research Part C: Emerging Technologies , 2021 , 128 : 103169 .
LUO J , WANG H , XU X H , et al . The influence of the spatial and temporal collocation windows on the comparisons of the ionospheric characteristic parameters derived from COSMIC radio occultation and digisondes [J ] . Advances in Space Research , 2019 , 63 ( 10 ): 3088 - 3101 .
PILLONI V , NING H S , ATZORI L . Task allocation among connected devices: requirements, approaches, and challenges [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 2 ): 1009 - 1023 .
SHIN H , LEE K , KWON H Y . A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark [J ] . The Journal of Supercomputing , 2022 , 78 ( 2 ): 2556 - 2579 .
WU Q , HE K W , CHEN X . Personalized federated learning for intelligent IoT applications: a cloud-edge based framework [J ] . IEEE Computer Graphics and Applications , 2020 , 1 : 35 - 44 .
XU C H , QU Y Y , XIANG Y , et al . Asynchronous federated learning on heterogeneous devices: a survey [J ] . Computer Science Review , 2023 , 50 : 100595 .
牛志升 . 面向6G网络的高可靠低延时通信计算与控制 [J ] . 中国科学(信息科学) , 2024 , 54 ( 5 ): 1267 - 1282 .
NIU Z S . uRLLC3: ultra-reliable and low-latency communication, computing, and control for 6G networks [J ] . Scientia Sinica (Informationis) , 2024 , 54 ( 5 ): 1267 - 1282 .
MA Q P , XU Y , XU H L , et al . FedSA: a semi-asynchronous federated learning mechanism in heterogeneous edge computing [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 12 ): 3654 - 3672 .
TANNER M A , WONG W H . The calculation of posterior distributions by data augmentation [J ] . Journal of the American Statistical Association , 1987 , 82 ( 398 ): 528 - 540 .
汤凌韬 , 王迪 , 刘盛云 . 面向非独立同分布数据的联邦学习数据增强方案 [J ] . 通信学报 , 2023 , 44 ( 1 ): 164 - 176 .
TANG L T , WANG D , LIU S Y . Data augmentation scheme for federated learning with non-IID data [J ] . Journal on Communications , 2023 , 44 ( 1 ): 164 - 176 .
DUAN M M , LIU D , CHEN X Z , et al . Astraea: self-balancing federated learning for improving classification accuracy of mobile deep learning applications [C ] // Proceedings of the 2019 IEEE 37th International Conference on Computer Design (ICCD) . Piscataway : IEEE Press , 2019 : 246 - 254 .
ZHANG H Y , CISSE M , DAUPHIN Y N , et al . Mixup: beyond empirical risk minimization [J ] . arXiv Preprint , arXiv: 1710.09412 , 2017 .
GOODFELLOW I J , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial networks [J ] . arXiv Preprint , arXiv: 1406.2261 , 2014 .
WANG H , KAPLAN Z , NIU D , et al . Optimizing federated learning on non-IID data with reinforcement learning [C ] // Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2020 : 1698 - 1707 .
KANG J W , XIONG Z H , NIYATO D , et al . Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 6 ): 10700 - 10714 .
SONG R , LYU L J , JIANG W , et al . V2X-boosted federated learning for cooperative intelligent transportation systems with contextual client selection [J ] . arXiv Preprint , arXiv: 2305.11654 , 2023 .
SANTOS C F G D , PAPA J P . Avoiding overfitting: a survey on regularization methods for convolutional neural networks [J ] . ACM Computing Surveys , 2022 , 54 ( 10 s): 1 - 25 .
蓝梦婕 , 蔡剑平 , 孙岚 . 非独立同分布数据下的自正则化联邦学习优化方法 [J ] . 计算机应用 , 2023 , 43 ( 7 ): 2073 - 2081 .
LAN M J , CAI J P , SUN L . Self-regularization optimization methods for non-IID data in federated learning [J ] . Journal of Computer Applications , 2023 , 43 ( 7 ): 2073 - 2081 .
HOSPEDALES T , ANTONIOU A , MICAELLI P , et al . Meta-learning in neural networks: a survey [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 9 ): 5149 - 5169 .
CHEN F , LUO M , DONG Z H , et al . Federated meta-learning with fast convergence and efficient communication [J ] . arXiv Preprint , arXiv: 1802.07876 , 2018 .
YUE S , REN J , XIN J , et al . Efficient federated meta-learning over multi-access wireless networks [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 5 ): 1556 - 1570 .
TIAN Y J , ZHAO X X , HUANG W . Meta-learning approaches for learning-to-learn in deep learning: a survey [J ] . Neurocomputing , 2022 , 494 : 203 - 223 .
张传尧 , 司世景 , 王健宗 , 等 . 联邦元学习综述 [J ] . 大数据 , 2023 , 9 ( 2 ): 122 - 146 .
ZHANG C Y , SI S J , WANG J Z , et al . Federated meta learning: a review [J ] . Big Data Research , 2023 , 9 ( 2 ): 122 - 146 .
MA X , SHAHBAKHTI M , CHIGAN C X . Connected vehicle based distributed meta-learning for online adaptive engine/powertrain fuel consumption modeling [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 9 ): 9553 - 9565 .
ZHANG Y , YANG Q . A survey on multi-task learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 12 ): 5586 - 5609 .
LI Z J , WU H , LU Y L . Coalition based utility and efficiency optimization for multi-task federated learning in Internet of vehicles [J ] . Future Generation Computer Systems , 2023 , 140 : 196 - 208 .
CHEN M H , JIANG M R , DOU Q , et al . FedSoup: improving generalization and personalization in federated learning via selective model interpolation [C ] // Lecture Notes in Computer Science . Berlin : Springer , 2023 : 318 - 328 .
MANSOUR Y , MOHRI M , RO J , et al . Three approaches for personalization with applications to federated learning [J ] . arXiv Preprint , arXiv: 2002.10619 , 2020 .
HANZELY F , RICHTÁRIK P . Federated learning of a mixture of global and local models [J ] . arXiv Preprint , arXiv: 2002.05516 , 2020 .
YANG Z K , LIU Y P , ZHANG S , et al . Personalized federated learning with model interpolation among client clusters and its application in smart home [J ] . World Wide Web , 2023 , 26 ( 4 ): 2175 - 2200 .
KEUPER J , PREUNDT F J . Distributed training of deep neural networks: theoretical and practical limits of parallel scalability [C ] // Proceedings of the 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC) . Piscataway : IEEE Press , 2016 : 19 - 26 .
SHOHAM N , AVIDOR T , KEREN A , et al . Overcoming forgetting in federated learning on non-IID data [J ] . arXiv Preprint , arXiv: 1910.07796 , 2019 .
LIU B Y , WANG L J , LIU M . Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems [J ] . IEEE Robotics and Automation Letters , 2019 , 4 ( 4 ): 4555 - 4562 .
YU X J , QUERALTA J P , WESTERLUND T . Towards lifelong federated learning in autonomous mobile robots with continuous sim-to-real transfer [J ] . Procedia Computer Science , 2022 , 210 : 86 - 93 .
ARIVAZHAGAN M G , AGGARWAL V , SINGH A K , et al . Federated learning with personalization layers [J ] . arXiv Preprint , arXiv: 1912.00818 , 2019 .
SU R Z , PANG X W , WANG H . A novel parameter decoupling approach of personalized federated learning for image analysis [J ] . Institution of Engineering and Technology Computer Vision , 2023 : 1 - 12 .
SONG R , XU R S , FESTAG A , et al . FedBEVT: federated learning bird’s eye view perception transformer in road traffic systems [J ] . IEEE Transactions on Intelligent Vehicles , 2024 , 9 ( 1 ): 958 - 969 .
BUCILUǍ C , CARUANA R , NICULESCU-MIZIL A . Model compression [C ] // Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM Press , 2006 : 535 - 541 .
HINTON G , VINYALS O , DEAN J . Distilling the knowledge in a neural network [J ] . arXiv Preprint , arXiv: 1503.02531 , 2015 .
SHUAI X , SHEN Y L , JIANG S Y , et al . BalanceFL: addressing class imbalance in long-tail federated learning [C ] // Proceedings of the 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) . Piscataway : IEEE Press , 2022 : 271 - 284 .
LIN T , KONG L J , STICH S U , et al . Ensemble distillation for robust model fusion in federated learning [J ] . arXiv Preprint , arXiv: 2006.07242 , 2020 .
PAPERNOT N , MCDANIEL P , WU X , et al . Distillation as a defense to adversarial perturbations against deep neural networks [C ] // Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP) . Piscataway : IEEE Press , 2016 : 582 - 597 .
DUCHI J , HAZAN E , SINGER Y . Adaptive subgradient methods for online learning and stochastic optimization [J ] . The Journal of Machine Learning Research , 2011 ( 12 ): 2121 - 2159 .
KINGMA D P , BA J . Adam: a method for stochastic optimization [J ] . arXiv Preprint , arXiv: 1412.6980 , 2014 .
SUTSKEVER I , MARTENS J , DAHL G , et al . On the importance of initialization and momentum in deep learning [C ] // Proceedings of the 30th International Conference on Machine Learning (ICML) . Piscataway : IEEE Press , 2013 : 1139 - 1147 .
REDDI S , CHARLES Z , ZAHEER M , et al . Adaptive federated optimization [J ] . arXiv Preprint , arXiv: 2003.00295 , 2020 .
陈飞扬 , 周晖 , 张一迪 . FCAT⁃FL: 基于Non⁃IID数据的高效联邦学习算法 [J ] . 南京邮电大学学报(自然科学版) , 2022 , 42 ( 3 ): 90 - 99 .
CHEN F Y , ZHOU H , ZHANG Y D . FCAT⁃FL: an efficient federated learning algorithm based on Non⁃IID data [J ] . Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition) , 2022 , 42 ( 3 ): 90 - 99 .
WU Y X , HE K M . Group normalization [C ] // European Conference on Computer Vision . Berlin : Springer , 2018 : 3 - 19 .
ZHANG Z M , YANG Y Q , YAO Z W , et al . Improving semi-supervised federated learning by reducing the gradient diversity of models [C ] // Proceedings of the 2021 IEEE International Conference on Big Data . Piscataway : IEEE Press , 2021 : 1214 - 1225 .
IOFFE S , SZEGEDY C . Batch normalization: accelerating deep network training by reducing internal covariate shift [C ] // Proceedings of the 32nd International Conference on International Conference on Machine Learning . New York : ACM Press , 2015 : 448 - 456 .
DU Z X , SUN J W , LI A , et al . Rethinking normalization methods in federated learning [C ] // Proceedings of the 3rd International Workshop on Distributed Machine Learning . New York : ACM Press , 2022 : 1 - 9 .
LIU L M , ZHANG J , SONG S H , et al . Client-edge-cloud hierarchical federated learning [C ] // Proceedings of the ICC 2020-2020 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2020 : 1 - 6 .
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 .
BRIGGS C , FAN Z , ANDRAS P . Federated learning with hierarchical clustering of local updates to improve training on non-IID data [C ] // Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN) . Piscataway : IEEE Press , 2020 : 1 - 9 .
ZHU H Y , FAN Y X , XIE Z P . Federated two-stage decoupling with adaptive personalization layers [J ] . Complex & Intelligent Systems , 2024 , 10 ( 3 ): 3657 - 3671 .
TAIK A , MLIKA Z , CHERKAOUI S . Clustered vehicular federated learning: process and optimization [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 12 ): 25371 - 25383 .
FedAI . An industrial grade federated learning framework [R ] . 2019 .
JING Q H , WANG W Y , ZHANG J X , et al . Quantifying the performance of federated transfer learning [C ] // Proceedings of the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality . Piscataway : IEEE Press , 2019 : 1 - 7 .
KATHEN M J T , JOHNSON P , FLORES I J , et al . AquaFeL-PSO: a monitoring system for water resources using autonomous surface vehicles based on multimodal PSO and federated learning [J ] . Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications , 2023 , 132 : 405 - 431 .
WANG S , HOSSEINALIPOUR S , GORLATOVA M , et al . UAV-assisted online machine learning over multi-tiered networks: a hierarchical nested personalized federated learning approach [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 2 ): 1847 - 1865 .
STADLER T , TRONCOSO C . Why the search for a privacy-preserving data sharing mechanism is failing [J ] . Nature Computational Science , 2022 , 2 : 208 - 210 .
SUN P J . Security and privacy protection in cloud computing: discussions and challenges [J ] . Journal of Network and Computer Applications , 2020 , 160 : 102642 .
BOSE S , MARIJAN D . A survey on privacy of health data lifecycle: a taxonomy, review, and future directions [J ] . arXiv Preprint , arXiv: 2311.05404 , 2023 .
金梁 , 楼洋明 , 孙小丽 , 等 . 6G无线内生安全理念与构想 [J ] . 中国科学(信息科学) , 2023 , 53 ( 2 ): 344 - 364 .
JIN L , LOU Y M , SUN X L , et al . Concept and vision of 6G wireless endogenous safety and security [J ] . Scientia Sinica (Informationis) , 2023 , 53 ( 2 ): 344 - 364 .
TZIAVOU O , PYTHAROULI S , SOUTER J . Unmanned aerial vehicle (UAV) based mapping in engineering geological surveys: considerations for optimum results [J ] . Engineering Geology , 2018 , 232 : 12 - 21 .
LI T , HU H T . Development of the use of unmanned aerial vehicles (UAVs) in emergency rescue in China [J ] . Risk Management and Healthcare Policy , 2021 , 14 : 4293 - 4299 .
MEKIKER B , PATEL A , WITTIE M P . Cost-effective situational awareness through IoT COTS radios [J ] . arXiv Preprint , arXiv: 2308. 12328 , 2023 .
HU X , CHU L Y , PEI J , et al . Model complexity of deep learning: a survey [J ] . Knowledge and Information Systems , 2021 , 63 ( 10 ): 2585 - 2619 .
LI Z H , HE Y H , YU H F , et al . Data heterogeneity-robust federated learning via group client selection in industrial IoT [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 18 ): 17844 - 17857 .
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 .
黄磊 , 易文姣 , 王英 , 等 . 基于联邦学习和多方安全计算的海铁联运数据安全共享方法研究 [J ] . 铁道运输与经济 , 2024 , 46 ( 4 ): 58 - 67 .
HUANG L , YI W J , WANG Y , et al . Research on secure data sharing methods for sea-rail intermodal transportation based on federated learning and multi-party secure computation [J ] . Railway Transport and Economy , 2024 , 46 ( 4 ): 58 - 67 .
黄知涛 , 柯达 , 王翔 . 电磁信号对抗样本攻击与防御发展研究 [J ] . 信息对抗技术 , 2023 , 2 ( 4 ): 37 - 52 .
HUANG Z T , KE D , WANG X . Survey of electromagnetic signal adversarial example attack and defense [J ] . Information Countermeasure Technology , 2023 , 2 ( 4 ): 37 - 52 .
尤肖虎 , 许威 , 相红 , 等 . 6G发展趋势与候选关键技术分析 [J ] . 信息通信技术 , 2023 , 17 ( 12 ): 11 - 27 .
YOU X H , XU W , XIANG H , et al . Analysis of 6G development trend and candidate key technologies [J ] . Information and Communication Technology , 2023 , 17 ( 12 ): 11 - 27 .
HUANG C W , ZAPPONE A , ALEXANDROPOULOS G C , et al . Reconfigurable intelligent surfaces for energy efficiency in wireless communication [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 8 ): 4157 - 4170 .
中国通信学会 . 通感算一体化网络前沿报告 [R ] . 2021 .
Chinese Society of Communications . Advanced report on integrated network of synsensing computing [R ] . 2021 .
ZHUANG W M , CHEN C , LYU L J . When foundation model meets federated learning: motivations, challenges, and future directions [J ] . arXiv Preprint , arXiv: 2306.15546 , 2023 .
JIANG F B , DONG L , TU S W , et al . Personalized wireless federated learning for large language models [J ] . arXiv Preprint , arXiv: 2404.13238 , 2024 .
XU H S , WU J , PAN Q Q , et al . A survey on digital twin for industrial Internet of things: applications, technologies and tools [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 4 ): 2569 - 2598 .
ZHANG Z C , LI C Y , SUN W , et al . A perceptual quality assessment exploration for AIGC images [C ] // Proceedings of the 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) . Piscataway : IEEE Press , 2023 : 440 - 445 .
0
浏览量
63
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构