An Overview of Federated Learning in Edge Intelligence

Zhang Xueqing; Liu Yanwei; Liu Jinxia; Han Yanni

doi:10.7544/issn1000-1239.202111100

Journal of Computer Research and Development > 2023 > 60(6): 1276-1295. > DOI: 10.7544/issn1000-1239.202111100 CSTR: 32373.14.issn1000-1239.202111100

Zhang Xueqing, Liu Yanwei, Liu Jinxia, Han Yanni. An Overview of Federated Learning in Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(6): 1276-1295. DOI: 10.7544/issn1000-1239.202111100

Citation:

PDF (1575 KB)

An Overview of Federated Learning in Edge Intelligence

1.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093
2.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049
3.
Zhejiang Wanli University, Ningbo, Zhejiang 315100

Funds: This work was supported by the National Natural Science Foundation of China (61771469) and the Cooperation Project Between Chongqing Municipal Undergraduate Universities and Institutes Affiliated to CAS (HZ2021015).

More Information

Author Bio:
Zhang Xueqing: born in 1994. Master. Her main research interest includes machine learning

Liu Yanwei: born in 1976. PhD, associate professor. Member of CCF. His main research interests include wireless communication, intelligent multimedia processing, and cyber security

Liu Jinxia: born in 1969. Master, professor. Her main research interests include wireless communication and edge intelligence

Han Yanni: born in 1981. PhD, associate professor. Her main research interests include wireless communication and intelligent data analysis
Received Date: November 07, 2021
Revised Date: September 15, 2022
Available Online: March 16, 2023

Graphical Abstract

Abstract

Abstract

With the increasing demand of edge intelligence, federated learning (FL) has been now of great concern to the industry. Compared with the traditionally centralized machine learning that is mostly based on cloud computing, FL collaboratively trains the neural network model over a large number of edge devices in a distributed way, without sending a large amount of local data to the cloud for processing, which makes the compute-extensive learning tasks sunk to the edge of the network closed to the user. Consequently, the users’ data can be trained locally to meet the needs of low latency and privacy protection. In mobile edge networks, due to the limited communication resources and computing resources, the performance of FL is subject to the integrated constraint of the available computation and communication resources during wireless networking, and also data quality in mobile device. Aiming for the applications of edge intelligence, the tough challenges for seeking high efficiency FL are analyzed here. Next, the research progresses in client selection, model training and model updating in FL are summarized. Specifically, the typical work in data unloading, model segmentation, model compression, model aggregation, gradient descent algorithm optimization and wireless resource optimization are comprehensively analyzed. Finally, the future research trends of FL in edge intelligence are prospected.
- federated learning,
- edge computing,
- edge intelligence,
- model aggregation,
- resource constraints

FullText(HTML)

References (133)

References

[1]	Mills J, Hu Jia, Min Geyong. Communication-efficient federated learning for wireless edge intelligence in IoT[J]. IEEE Internet of Things Journal, 2019, 7(7): 5986−5994
[2]	Covington P, Adams J, Sargin E. Deep neural networks for YouTube recommendations[C] //Proc of the 10th ACM Conf on Recommender Systems. New York: ACM, 2016: 191−198
[3]	Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition[C] //Proc of the 15th IEEE Int Conf on Computer Vision Workshop. Piscataway, NJ: IEEE, 2015: 258−266
[4]	Mowla N I, Tran N H, Doh I, et al. Federated learning-based cognitive detection of jamming attack in flying ad-hoc network[J]. IEEE Access, 2020, 8: 4338−4350 doi: 10.1109/ACCESS.2019.2962873
[5]	Brik B, Ksentini A, Bouaziz M. Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems[J]. IEEE Access, 2020, 8: 53841−53849 doi: 10.1109/ACCESS.2020.2981430
[6]	Abbas N, Zhang Yan, Taherkordi A, et al. Mobile edge computing: A survey[J]. IEEE Internet of Things Journal, 2017, 5(1): 450−465
[7]	Mcmahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data[C] //Proc of the 20th Int Conf on Artificial Intelligence and Statistics. New York: PMLR, 2017 : 1273−1282.
[8]	Yang Qiang, Liu Yang, Chen Tianjian, et al. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 1−19
[9]	Zhou Zhi, Yang Song, Pu Lingjun, et al. CEFL: Online admission control, data scheduling, and accuracy tuning for cost-efficient federated learning across edge nodes[J]. IEEE Internet of Things Journal, 2020, 7(10): 9341−9356 doi: 10.1109/JIOT.2020.2984332
[10]	Ruder S. An overview of gradient descent optimization algorithms[J]. arXiv preprint, arXiv: 1609.04747, 2016
[11]	Lim W Y B, Luong N C, Hoang D T, et al. Federated learning in mobile edge networks: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031−2063
[12]	Li Tian, Sahu A K, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50−60 doi: 10.1109/MSP.2020.2975749
[13]	Li Qinbin, Wen Zeyi, Wu Zhaomin, et al. A survey on federated learning systems: Vision, hype and reality for data privacy and protection[J]. arXiv preprint, arXiv: 1907.09693, 2019
[14]	Wang Xiaofei, Han Yiwen, Wang Chenyang, et al. In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156−165 doi: 10.1109/MNET.2019.1800286
[15]	Kairouz P, Mcmahan H B, Avent B, et al. Advances and open problems in federated learning[J]. arXiv preprint, arXiv: 1912.04977, 2019
[16]	王艳,李念爽,王希龄,等. 编码技术改进大规模分布式机器学习性能综述[J]. 计算机研究与发展,2020,57(3):542−561 doi: 10.7544/issn1000-1239.2020.20190286 Wang Yan, Li Nianshuang, Wang Xiling, et al. Coding-based performance improvement of distributed machine learning in large-scale clusters[J]. Journal of Computer Research and Development, 2020, 57(3): 542−561 (in Chinese) doi: 10.7544/issn1000-1239.2020.20190286
[17]	Jin Yibo, Jiao Lei, Qian Zhuzhong, et al. Resource-efficient and convergence-preserving online participant selection in federated learning[C] //Proc of the 40th IEEE Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE, 2020: 606−616
[18]	Chai Z, Ali A, Zawad S, et al. TiFL: A tier-based federated learning system[C] //Proc of the 29th Int Symp on High-Performance Parallel and Distributed Computing. New York: ACM, 2020: 125−136
[19]	Li Li, Xiong Haoyi, Guo Zhishan, et al. SmartPC: Hierarchical pace control in real-time federated learning system[C] //Proc of the 40th IEEE Real-Time Systems Symp (RTSS). Piscataway, NJ: IEEE, 2019: 406−418
[20]	Khan L U, Alsenwi M, Han Zhu, et al. Self organizing federated learning over wireless networks: A socially aware clustering approach[C] //Proc of the 34th Int Conf on Information Networking (ICOIN). Piscataway, NJ: IEEE, 2020: 453−458
[21]	Xu Jie, Wang Heqiang. Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective[J]. IEEE Transactions on Wireless Communications, 2020, 20(2): 1188−1200
[22]	Damaskinos G, Guerraoui R, Kermarrec A M, et al. Fleet: Online federated learning via staleness awareness and performance prediction[C] //Proc of the 21st Int Middleware Conf. New York: ACM, 2020: 163−177
[23]	Sprague M R, Jalalirad A, Scavuzzo M, et al. Asynchronous federated learning for geospatial applications[C] //Proc of the Joint European Conf on Machine Learning and Knowledge Discovery in Databases. Cham, Switzerland: Springer, 2018: 21−28
[24]	Wu Wentai, He Ligang, Lin Weiwei, et al. Safa: A semi-asynchronous protocol for fast federated learning with low overhead[J]. IEEE Transactions on Computers, 2020, 70(5): 655−668
[25]	Nishio T, Yonetani R. Client selection for federated learning with heterogeneous resources in mobile edge[C/OL] //Proc of the 53rd IEEE Int Conf on Communications. Piscataway, NJ: IEEE, 2019[2022-09-05].https://ieeexplore.ieee.org/document/8761315
[26]	Yoshida N, Nishio T, Morikura M, et al. Hybrid-FL for wireless networks: Cooperative learning mechanism using non-IID data[C/OL] //Proc of the 54th IEEE Int Conf on Communications (ICC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9149323
[27]	Khan L U, Pandey S R, Tran N H, et al. Federated learning for edge networks: Resource optimization and incentive mechanism[J]. IEEE Communications Magazine, 2020, 58(10): 88−93 doi: 10.1109/MCOM.001.1900649
[28]	Kang Jiawen, Xiong Zehui, 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 doi: 10.1109/JIOT.2019.2940820
[29]	Kim H, Park J, Bennis M, et al. Blockchained on-device federated learning[J]. IEEE Communications Letters, 2019, 24(6): 1279−1283
[30]	Li Tian, Sanjabi M, Beirami A, et al. Fair resource allocation in federated learning[J]. arXiv preprint, arXiv: 1905.10497, 2020
[31]	Pandey S R, Tran N H, Bennis M, et al. A crowdsourcing framework for on-device federated learning[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3241−3256 doi: 10.1109/TWC.2020.2971981
[32]	Le T H T, Tran N H, Tun Y K, et al. Auction based incentive design for efficient federated learning in cellular wireless networks[C/OL] //Proc of the IEEE Wireless Communications and Networking Conf (WCNC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9120773
[33]	Jiao Yutao, Wang Ping, Niyato D, et al. Toward an automated auction framework for wireless federated learning services market[J]. IEEE Transactions on mobile Computing, 2020, 20(10): 3034−3048
[34]	Gao Xiaozheng, Wang Ping, Niyato D, et al. Auction-based time scheduling for backscatter-aided RF-powered cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1684−1697 doi: 10.1109/TWC.2019.2895340
[35]	Ko BongJun, Wang Shiqiang, He Ting, et al. On data summarization for machine learning in multi-organization federations[C] //Proc of the 7th IEEE Int Conf on Smart Computing (SMARTCOMP). Piscataway, NJ: IEEE, 2019: 63−68
[36]	Valerio L, Passarella A, Conti M. Optimal trade-off between accuracy and network cost of distributed learning in mobile edge Computing: An analytical approach[C/OL] //Proc of the 18th Int Symp on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). Piscataway, NJ: IEEE, 2017[2022-09-05].https://ieeexplore.ieee.org/abstract/document/7974310
[37]	Skatchkovsky N, Simeone O. Optimizing pipelined computation and communication for latency-constrained edge learning[J]. IEEE Communications Letters, 2019, 23(9): 1542−1546 doi: 10.1109/LCOMM.2019.2922658
[38]	Huang Yutao, Zhu Yifei, Fan Xiaoyi, et al. Task scheduling with optimized transmission time in collaborative cloud-edge learning[C/OL] //Proc of the 27th Int Conf on Computer Communication and Networks (ICCCN). Piscataway, NJ: IEEE, 2018[2022-09-05].https://ieeexplore.ieee.org/abstract/document/8487352
[39]	Dey S, Mukherjee A, Pal A, et al. Partitioning of CNN models for execution on fog devices[C] //Proc of the 1st ACM Int Workshop on Smart Cities and Fog Computing. New York: ACM, 2018: 19−24
[40]	Zhang Shigeng, Li Yinggang, Liu Xuan, et al. Towards real-time cooperative deep inference over the cloud and edge end devices[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 1−24
[41]	Dey S, Mukherjee A, Pal A. Embedded deep inference in practice: Case for model partitioning[C] //Proc of the 1st Workshop on Machine Learning on Edge in Sensor Systems. New York: ACM, 2019: 25−30
[42]	Lin Bing, Huang Yinhao, Zhang Jianshan, et al. Cost-driven off-loading for DNN-based applications over cloud, edge, and end devices[J]. IEEE Transactions on Industrial Informatics, 2019, 16(8): 5456−5466
[43]	Wang Lingdong, Xiang Liyao, Xu Jiayu, et al. Context-aware deep model compression for edge cloud computing[C] //Proc of the 40th Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE, 2020: 787−797
[44]	Wang Ji, Zhang Jianguo, Bao Weidong, et al. Not just privacy: Improving performance of private deep learning in mobile cloud[C] //Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2018: 2407−2416
[45]	Zhang Jiale, Wang Junyu, Zhao Yanchao, et al. An efficient federated learning scheme with differential privacy in mobile edge computing[C] //Proc of the Int Conf on Machine Learning and Intelligent Communications. Berlin: Springer, 2019: 538−550
[46]	Ivkin N, Rothchild D, Ullah E, et al. Communication-efficient distributed SGD with sketching[J]. Advances in Neural Information Processing Systems, 2019, 32: 13144−13154
[47]	Zhang Boyu, Davoodi A, Hu Yuhen. Exploring energy and accuracy tradeoff in structure simplification of trained deep neural networks[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018, 8(4): 836−84 doi: 10.1109/JETCAS.2018.2833383
[48]	Konen J, Mcmahan H B, Yu F X, et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint, arXiv: 1610.05492, 2016
[49]	Caldas S, Konečny J, Mcmahan H B, et al. Expanding the reach of federated learning by reducing client resource requirements[J]. arXiv preprint, arXiv: 1812.07210, 2018
[50]	Rothchild D, Panda A, Ullah E, et al. FetchSGD: Communication-efficient federated learning with sketching[C] //Proc of the 37th Int Conf on Machine Learning. New York: PMLR, 2020: 8253−8265
[51]	Jeong E, Oh S, Kim H, et al. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data[J]. arXiv preprint, arXiv: 1811.11479, 2018
[52]	Ahn J H, Simeone O, Kang J. Wireless federated distillation for distributed edge learning with heterogeneous data[C/OL] //Proc of the 30th Annual Int Symp on Personal, Indoor and Mobile Radio Communications (PIMRC). Piscataway, NJ: IEEE, 2019[2022-09-05]. https://ieeexplore.ieee.org/abstract/document/8904164
[53]	Reisizadeh A, Mokhtari A, Hassani H, et al. FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization[C] //Proc of the 23rd Int Conf on Artificial Intelligence and Statistics. New York: PMLR, 2020: 2021−2031
[54]	Karimireddy S P, Kale S, Mohri M, et al. SCAFFOLD: Stochastic controlled averaging for federated learning[C] //Proc of the 37th Int Conf on Machine Learning. New York: PMLR, 2020: 5132−5143
[55]	Li Tian, Sahu A K, Zaheer M, et al. Federated optimization in heterogeneous networks[J]. Proceedings of Machine Learning and Systems, 2020, 2: 429−450
[56]	Wang Hongyi, Yurochkin M, Sun Yuekai, et al. Federated learning with matched averaging[J]. arXiv preprint, arXiv: 2002.06440, 2020
[57]	Pillutla K, Kakade S M, Harchaoui Z. Robust aggregation for federated learning[J]. IEEE Transactions on Signal Processing, 2022, 70: 1142−1154 doi: 10.1109/TSP.2022.3153135
[58]	Grama M, Musat M, Muñoz-González L, et al. Robust aggregation for adaptive privacy preserving federated learning in healthcare[J]. arXiv preprint, arXiv: 2009.08294, 2020
[59]	Ang Fan, Chen Li, Zhao Nan, et al. Robust federated learning with noisy communication[J]. IEEE Transactions on Communications, 2020, 68(6): 3452−3464 doi: 10.1109/TCOMM.2020.2979149
[60]	Lu Yanyang, Fan Lei. An efficient and robust aggregation algorithm for learning federated CNN[C/OL] //Proc of the 3rd Int Conf on Signal Processing and Machine Learning. New York: ACM, 2020[2022-09-05].https://dl.acm.org/doi/abs/10.1145/3432291.3432303
[61]	Chen Zhou, Lv Na, Liu Pengfei, et al. Intrusion detection for wireless edge networks based on federated learning[J]. IEEE Access, 2020, 8: 217463−217472 doi: 10.1109/ACCESS.2020.3041793
[62]	So J, Güler B, Avestimehr A S. Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning[J]. IEEE Journal on Selected Areas in Information Theory, 2021, 2(1): 479−489 doi: 10.1109/JSAIT.2021.3054610
[63]	Wang Shiqiang, 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 doi: 10.1109/JSAC.2019.2904348
[64]	Zhang Xiongtao, Zhu Xiaomin, Wang Ji, et al. Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks[J]. Information Sciences, 2020, 540(5): 242−262
[65]	Liu Lumin, Zhang Jun, Song Shenghui, et al. Client-edge-cloud hierarchical federated learning[C/OL] //Proc of the 54th IEEE Int Conf on Communications (ICC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9148862
[66]	Mohammad U, Sorour S. Adaptive task allocation for mobile edge learning[C/OL] //Proc of the Wireless Communications and Networking Conf Workshop (WCNCW). Piscataway, NJ: IEEE, 2019[2022-09-05].https://ieeexplore.ieee.org/abstract/document/8902527
[67]	Jiang Hui, Liu Min, Yang Bo, et al. Customized federated learning for accelerated edge computing with heterogeneous task targets[J]. Computer Networks, 2020, 183(12): 107569−107569
[68]	Lin Yujun, Han Song, Mao Huizi, et al. Deep gradient compression: Reducing the communication bandwidth for distributed training[J]. arXiv preprint, arXiv: 1712.01887, 2017
[69]	Liu Wei, Chen Li, Chen Yunfei, et al. Accelerating federated learning via momentum gradient descent[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(8): 1754−1766 doi: 10.1109/TPDS.2020.2975189
[70]	Abdi A, Saidutta Y M, Fekri F. Analog compression and communication for federated learning over wireless MAC[C/OL] //Proc of the 21st Int Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway, NJ: IEEE, 2020[2022-09-05]. https://ieeexplore.ieee.org/abstract/document/9154309
[71]	Alistarh D, Grubic D, Li J, et al. QSGD: Communication-efficient SGD via gradient quantization and encoding[J]. Advances in Neural Information Processing Systems, 2017, 30: 1709−1720
[72]	Bernstein J, Wang Yuxiang, Azizzadenesheli K, et al. signSGD: Compressed optimisation for non-convex problems[C] //Proc of the 35th Int Conf on Machine Learning. New York: PMLR, 2018: 560−569
[73]	Zhu Guangxu, Wang Yong, Huang Kaibin. Broadband analog aggregation for low-latency federated edge learning[J]. IEEE Transactions on Wireless Communications, 2019, 19(1): 491−506
[74]	Amiri M M, Gündüz D. Federated learning over wireless fading channels[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3546−3557 doi: 10.1109/TWC.2020.2974748
[75]	Wu Jiaxiang, Huang Weidong, Huang Junzhou, et al. Error compensated quantized SGD and its applications to large-scale distributed optimization[C] //Proc of the 35th Int Conf on Machine Learning. New York: PMLR, 2018: 5325−5333
[76]	Basu D, Data D, Karakus C, et al. Qsparse-local-SGD: Distributed SGD with quantization, sparsification, and local computations[J]. arXiv preprint, arXiv: 1906.02367, 2019
[77]	Xin Ran, Kar S, Khan U A. An introduction to decentralized stochastic optimization with gradient tracking[J]. arXiv preprint, arXiv: 1907.09648, 2019
[78]	Haddadpour F, Kamani M M, Mokhtari A, et al. Federated learning with compression: Unified analysis and sharp guarantees[C] //Proc of the 24th Int Conf on Artificial Intelligence and Statistics. New York: PMLR, 2021: 2350−2358
[79]	Tang Hanlin, Lian Xiangru, Yan Ming, et al. D²: Decentralized training over decentralized data[C] //Proc of the 35th Int Conf on Machine Learning. New York: PMLR, 2018: 4848−4856
[80]	Amiri M M, Gündüz D. Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air[J]. IEEE Transactions on Signal Processing, 2020, 68(1): 2155−2169
[81]	Zhu Guangxu, Du Yuqing, Gündüz D, et al. One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis[J]. IEEE Transactions on Wireless Communications, 2020, 20(3): 2120−2135
[82]	Lu Yunlong, Huang Xiaohong, Dai Yueyue, et al. Differentially private asynchronous federated learning for mobile edge computing in urban informatics[J]. IEEE Transactions on Industrial Informatics, 2019, 16(3): 2134−2143
[83]	Sun Jun, Chen Tianyi, Giannakis G B, et al. Communication-efficient distributed learning via lazily aggregated quantized gradients[J]. arXiv preprint, arXiv: 1909.07588, 2019
[84]	Shokri R, Shmatikov V. Privacy-preserving deep learning[C] //Proc of the 22nd ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2015: 1310−1321
[85]	Elgabli A, Park J, Bedi A S, et al. Q-GADMM: Quantized group ADMM for communication efficient decentralized machine learning[J]. IEEE Transactions on Communications, 2020, 69(1): 164−181
[86]	Elgabli A, Park J, Bedi A S, et al. GADMM: Fast and communication efficient framework for distributed machine learning[J]. Journal of Machine Learning Research, 2020, 21(76): 1−39
[87]	Elgabli A, Park J, Ahmed S, et al. L-FGADMM: Layer-wise federated group ADMM for communication efficient decentralized deep learning[C/OL] //Proc of the IEEE Wireless Communications and Networking Conf(WCNC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9120758
[88]	Zhang Wei, Gupta S, Lian Xiangru, et al. Staleness-aware async-SGD for distributed deep learning[J]. arXiv preprint, arXiv: 1511.05950, 2015
[89]	Tao Zeyi, Li Qun. eSGD: Communication efficient distributed deep learning on the edge[C/OL] //Proc of the 1st USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18). Berkeley, CA: USENIX Association, 2018[2022-09-05].https://www.usenix.org/conference/hotedge18/presentation/tao
[90]	Wang Luping, Wang Wei, Li Bo. CMFL: Mitigating communication overhead for federated learning[C] //Proc of the 39th Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE: 954−964
[91]	Xing Hong, Simeone O, Bi Suzhi. Decentralized federated learning via SGD over wireless D2D networks[C/OL] //Proc of the 21st Int Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9154332
[92]	Shiri H, Park J, Bennis M. Communication-efficient massive UAV online path control: Federated learning meets mean-field game theory[J]. IEEE Transactions on Communications, 2020, 68(11): 6840−6857 doi: 10.1109/TCOMM.2020.3017281
[93]	Zeng Tengchan, Semiari O, Mozaffari M, et al. Federated learning in the sky: Joint power allocation and scheduling with UAV swarms[C/OL] //Proc of the 54th IEEE Int Conf on Communications (ICC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9148776
[94]	Pham Q V, Zeng Ming, Ruby R, et al. UAV communications for sustainable federated learning[J]. IEEE Transactions on Vehicular Technology, 2021, 70(4): 3944−3948 doi: 10.1109/TVT.2021.3065084
[95]	Fadlullah Z M, Kato N. HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks[J]. IEEE Transactions on Emerging Topics in Computing, 2020, 10(1): 112−123
[96]	Chen Mingzhe, Mozaffari M, Saad W, et al. Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5): 1046−1061 doi: 10.1109/JSAC.2017.2680898
[97]	Lahmeri M A, Kishk M A, Alouini M S. Artificial intelligence for UAV-enabled wireless networks: A survey[J]. IEEE Open Journal of the Communications Society, 2021, 2: 1015−1040 doi: 10.1109/OJCOMS.2021.3075201
[98]	Wang Yuntao, Su Zhou, Zhang Ning, et al. Learning in the air: Secure federated learning for UAV-assisted crowdsensing[J]. IEEE Transactions on Network Science and Engineering, 2020, 8(2): 1055−1069
[99]	Lim W Y B, Huang Jianqiang, Xiong Zehui, 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 doi: 10.1109/TITS.2021.3056341
[100]	Samarakoon S, Bennis M, Saad W, et al. Distributed federated learning for ultra-reliable low-latency vehicular communications[J]. IEEE Transactions on Communications, 2019, 68(2): 1146−1159
[101]	Ye Dongdong, Yu Rong, Pan Miao, et al. Federated learning in vehicular edge computing: A selective model aggregation approach[J]. IEEE Access, 2020, 8: 23920−23935 doi: 10.1109/ACCESS.2020.2968399
[102]	Lu Yunlong, Huang Xiaohong, Dai Yueyue, et al. Federated learning for data privacy preservation in vehicular cyber-physical systems[J]. IEEE Network, 2020, 34(3): 50−56 doi: 10.1109/MNET.011.1900317
[103]	Du Zhaoyang, Wu Celimuge, Yoshinaga T, et al. Federated learning for vehicular Internet of things: Recent advances and open issues[J]. IEEE Open Journal of the Computer Society, 2020, 1: 45−61 doi: 10.1109/OJCS.2020.2992630
[104]	Deveaux D, Higuchi T, Uçar S, et al. On the orchestration of federated learning through vehicular knowledge networking[C/OL] //Proc of IEEE Vehicular Networking Conf (VNC). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9318386
[105]	Chen Mingzhe, Semiari O, Saad W, et al. Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks[J]. IEEE Transactions on Wireless Communications, 2019, 19(1): 177−191
[106]	Mozaffari M, Saad W, Bennis M, et al. A tutorial on UAVs for wireless networks: Applications, challenges, and open problems[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2334−2360
[107]	Samarakoon S, Bennis M, Saad W, et al. Federated learning for ultra-reliable low-latency V2V communications[C/OL] //Proc of the IEEE Global Communications Conf (GLOBECOM). Piscataway, NJ: IEEE, 2018[2022-09-05].https://ieeexplore.ieee.org/abstract/document/8647927
[108]	Feyzmahdavian H R, Aytekin A, Johansson M. An asynchronous mini-batch algorithm for regularized stochastic optimization[J]. IEEE Transactions on Automatic Control, 2016, 61(12): 3740−3754 doi: 10.1109/TAC.2016.2525015
[109]	Lu Yunlong, Huang Xiaohong, Zhang Ke, et al. Blockchain empowered asynchronous federated learning for secure data sharing in Internet of vehicles[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4298−4311 doi: 10.1109/TVT.2020.2973651
[110]	Yin Feng, Lin Zhidi, Kong Qinglei, et al. FedLoc: Federated learning framework for data-driven cooperative localization and location data processing[J]. IEEE Open Journal of Signal Processing, 2020, 1: 187−215 doi: 10.1109/OJSP.2020.3036276
[111]	Merluzzi M, Di Lorenzo P, Barbarossa S. Dynamic resource allocation for wireless edge machine learning with latency and accuracy guarantees[C] //Proc of the 45th IEEE Int Conf on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE, 2020: 9036−9040
[112]	Yang Zhaohui, Chen Mingzhe, Saad W, et al. Energy efficient federated learning over wireless communication networks[J]. IEEE Transactions on Wireless Communications, 2020, 20(3): 1935−1949
[113]	Luo Siqi, Chen Xu, Wu Qiong, et al. Hfel: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6535−6548 doi: 10.1109/TWC.2020.3003744
[114]	Abad M S H, Ozfatura E, Gunduz D, et al. Hierarchical federated learning across heterogeneous cellular networks[C] //Proc of the 45th IEEE Int Conf on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE, 2020: 8866−8870
[115]	Liu Dongzhu, Zhu Guangxu, Zhang Jun, et al. Data-importance aware user scheduling for communication-efficient edge machine learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 7(1): 265−278
[116]	Zhan Yufeng, Li Peng, Guo Song. Experience-driven computational resource allocation of federated learning by deep reinforcement learning[C] //Proc of the 34th 2020 IEEE Int Parallel and Distributed Processing Symp (IPDPS). Piscataway, NJ: IEEE, 2020: 234−243
[117]	Zeng Qunsong, Du Yuqing, Huang Kaibin, et al. Energy-efficient radio resource allocation for federated edge learning[C/OL] //Proc of the 54th 2020 IEEE Intl Conf on Communications Workshops (ICC Workshops). Piscataway, NJ: IEEE, 2020[2022-09-05]. https://ieeexplore.ieee.org/abstract/document/9145118
[118]	Chen Mingzhe, Poor H V, Saad W, et al. Convergence time optimization for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2020, 20(4): 2457−2471
[119]	Mo Xiaopeng, Xu Jie. Energy-efficient federated edge learning with joint communication and computation design[J]. Journal of Communications and Information Networks, 2021, 6(2): 110−124 doi: 10.23919/JCIN.2021.9475121
[120]	Ren Jinke, Yu Guanding, Ding Guangyao. Accelerating DNN training in wireless federated edge learning systems[J]. IEEE Journal on Selected Areas in Communications, 2020, 39(1): 219−232
[121]	Anh T T, Luong N C, Niyato D, et al. Efficient training management for mobile crowd-machine learning: A deep reinforcement learning approach[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1345−1348 doi: 10.1109/LWC.2019.2917133
[122]	Nguyen H T, Luong N C, Zhao J, et al. Resource allocation in mobility-aware federated learning networks: A deep reinforcement learning approach[C/OL] //Pro of the 6th World Forum on Internet of Things (WF-IoT). Piscataway, NJ: IEEE, 2020[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9221089
[123]	Zhang Xueqing, Liu Yanwei, Liu Jinxia, et al. D2D-assisted federated learning in mobile edge computing networks [C/OL] //Pro of the 2021 IEEE Wireless Communications and Networking Conf (WCNC). Piscataway, NJ: IEEE, 2021[2022-09-05].https://ieeexplore.ieee.org/abstract/document/9417459
[124]	Yang Kai, Jiang Tao, Shi Yuanming, et al. Federated learning via over-the-air computation[J]. IEEE Transactions on Wireless Communications, 2020, 19(3): 2022−2035 doi: 10.1109/TWC.2019.2961673
[125]	Qin Zhijin, Li G Y, Ye Hao. Federated learning and wireless communications[J]. IEEE Wireless Communications, 2021, 28(5): 134−140 doi: 10.1109/MWC.011.2000501
[126]	Amiria M M, Dumanb T M, Gündüzc D, et al. Collaborative machine learning at the wireless edge with blind transmitters[C/OL] //Proc of the 7th IEEE Global Conf on Signal and Information Processing. Piscataway, NJ: IEEE, 2019[2022-09-05].https://iris.unimore.it/handle/11380/1202665
[127]	Chen Mingzhe, Yang Zhaohui, Saad W, et al. A joint learning and communications framework for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2020, 20(1): 269−283
[128]	Yang H H, Arafa A, Quek T Q, et al. Age-based scheduling policy for federated learning in mobile edge networks[C] //Proc of the 45th IEEE Int Conf on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE: 8743−8747
[129]	Dinh C, Tran N H, Nguyen M N, et al. Federated learning over wireless networks: Convergence analysis and resource allocation[J]. IEEE/ACM Transactions on Networking, 2020, 29(1): 398−409
[130]	Yang Hao, Liu Zuozhu, Quek T Q, et al. Scheduling policies for federated learning in wireless networks[J]. IEEE Transactions on Communications, 2019, 68(1): 317−333
[131]	Shi Wenqi, Zhou Sheng, Niu Zhisheng. Device scheduling with fast convergence for wireless federated learning[C/OL] //Proc of the 54th IEEE Int Conf on Communications (ICC). Piscataway, NJ: IEEE, 2020[2022-09-05]. https://ieeexplore.ieee.org/abstract/document/9149138
[132]	Amiri M M, Gündüz D, Kulkarni S R, et al. Update aware device scheduling for federated learning at the wireless edge[C] //Proc of the 2020 IEEE Int Symp on Information Theory (ISIT). Piscataway, NJ: IEEE, 2020: 2598−2603
[133]	Bonawitz K, Ivanov V, Kreuter B, et al. Practical secure aggregation for privacy-preserving machine learning[C] //Proc of the ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2017: 1175−1191

Cited By

Cited by

Periodical cited type(12)

1.	刘向举，李金贺，方贤进，王宇. 移动边缘计算中计算卸载与资源分配联合优化策略. 计算机工程与科学. 2024(03): 416-426 .
2.	闾国年，袁林旺，陈旻，张雪英，周良辰，俞肇元，罗文，乐松山，吴明光. 地理信息学科发展的思考. 地球信息科学学报. 2024(04): 767-778 .
3.	谢满德，黄竹芳，孙浩. 云边端协同下多用户细粒度任务卸载调度策略. 电信科学. 2024(04): 107-121 .
4.	纪允，孙建明，夏涛，吴子良，叶旭琪. 基于多层次数据协同应用的海关数据安全机制研究. 中国口岸科学技术. 2024(05): 27-34 .
5.	方浩添，田乐，郭茂祖. 基于多群体混合智能优化算法的卸载决策寻优方法. 智能系统学报. 2024(06): 1573-1583 .
6.	牟琦，韩嘉嘉，张寒，李占利. 基于云边协同的煤矿井下尺度自适应目标跟踪方法. 工矿自动化. 2023(04): 50-61 .
7.	陆嘉旻，蒋丞，柴俊，贺亚龙，漆昭铃. 基于云边端协同的UUV数字模型设计与实现. 电声技术. 2023(03): 31-35 .
8.	何牧，孙越，庞琦方. 基于边缘计算的智能视频分析算法研究. 电力大数据. 2023(04): 65-73 .
9.	王宏杰，徐胜超，陈刚，杨波，毛明扬. 基于萤火虫算法的移动边缘计算网络带宽优化策略. 计算机测量与控制. 2023(11): 280-285 .
10.	张俊娜，鲍想，陈家伟，赵晓焱，袁培燕，王尚广. 一种联合时延和能耗的依赖性任务卸载方法. 计算机研究与发展. 2023(12): 2770-2782 . 本站查看
11.	邱丹青，许宇辉. 5G移动边缘计算环境下的任务卸载方法研究. 企业科技与发展. 2023(12): 75-78 .
12.	林铭敏. 基于目标追踪的视频边缘计算云边协同任务调度及信息安全管理. 信息与电脑(理论版). 2023(20): 63-65 .