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《工程(英文)》 >> 2022年 第8卷 第1期 doi: 10.1016/j.eng.2021.12.002

6G中联邦学习的应用、挑战和机遇

a Department of Electronic and Electrical Engineering, University College London, London WC1E 6BT, UK
b Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
c Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong, Shenzhen 518172, China
d School of Science and Engineering and Future Network of Intelligence Institute, the Chinese University of Hong Kong, Shenzhen 518172, China

收稿日期: 2021-01-01 修回日期: 2021-06-13 录用日期: 2021-10-15 发布日期: 2021-12-08

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摘要

标准的机器学习方法需要在数据中心集中训练数据,从而采用集中式机器学习算法来进行数据分析和推理。然而,由于无线网络中的隐私限制以及无线通信资源受限,边缘设备将数据传输到参数服务器通常是不可取和不切实际的。联邦学习可解决这些问题。联邦学习可以使设备能够在没有数据共享和传输的情况下训练机器学习模型。本文全面概述了未来第六代(6G)无线网络的联邦学习应用。特别是,首先描述了将联邦学习应用于无线通信中的基本要求。然后详细介绍了无线通信中潜在的联邦学习新型应用,讨论了与新型应用相关的主要问题和挑战。最后,描述了用于无线通信的联邦学习的详细实现方案,并给出了联邦学习的难点和应用前景。

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参考文献

[ 1 ] Saad W, Bennis M, Chen M. A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw 2020;34 (3):134–42. 链接1

[ 2 ] Chen M, Yang Z, Saad W, Yin C, Poor HV, Cui S. A joint learning and communications framework for federated learning over wireless networks. IEEE Trans Wirel Commun 2021;20(1):269–83. 链接1

[ 3 ] Konecˇny´ J, McMahan HB, Ramage D, Richtárik P. Federated optimization: distributed machine learning for on-device intelligence. 2016. arXiv: 1610.02527.

[ 4 ] Bennis M, Debbah M, Huang K, Yang Z. Guest editorial: communication technologies for efficient edge learning. IEEE Commun Mag 2020;58(12):12–3. 链接1

[ 5 ] Zhu G, Wang Y, Huang K. Broadband analog aggregation for low-latency federated edge learning. IEEE Trans Wirel Commun 2020;19(1):491–506. 链接1

[ 6 ] Zhu G, Du Y, Gunduz D, Huang K. One-bit over-the-air aggregation for communication-efficient federated edge learning: design and convergence analysis. 2020. arXiv: 2001.05713.

[ 7 ] Zeng Q, Du Y, Huang K, Leung KK. Energy-efficient resource management for federated edge learning with CPU–GPU heterogeneous computing. 2020. arXiv: 2007.07122.

[ 8 ] Mohammadi Amiri M, Gunduz D. Machine learning at the wireless edge: distributed stochastic gradient descent over-the-air. IEEE Trans Signal Process 2020;68:2155–69. 链接1

[ 9 ] Gunduz D, Kurka DB, Jankowski M, Amiri MM, Ozfatura E, Sreekumar S. Communicate to learn at the edge. IEEE Commun Mag 2020;58(12):14–9. 链接1

[10] Amiri MM, Gunduz D. Federated learning over wireless fading channels. IEEE Trans Wirel Commun 2020;19(5):3546–57. 链接1

[11] Hosseinalipour S, Brinton CG, Aggarwal V, Dai H, Chiang M. From federated learning to fog learning: towards large-scale distributed machine learning in heterogeneous wireless networks. 2020. arXiv: 2006.03594.

[12] Liu Y, Yuan X, Xiong Z, Kang J, Wang X, Niyato D. Federated learning for 6G communications: challenges, methods, and future directions. China Commun 2020;17(9):105–18. 链接1

[13] Hosseinalipour S, Azam SS, Brinton CG, Michelusi N, Aggarwal V, Love DJ, et al. Multi-stage hybrid federated learning over large-scale wireless fog networks. 2020. arXiv: 2007.09511.

[14] Jin R, He X, Dai H. On the design of communication efficient federated learning over wireless networks. 2020. arXiv: 2004.07351.

[15] Liu D, Simeone O. Privacy for free: wireless federated learning via uncoded transmission with adaptive power control. IEEE J Select Areas Commun 2021;39(1):170–85. 链接1

[16] Kassab R, Simeone O. Federated generalized Bayesian learning via distributed stein variational gradient descent. 2020. arXiv: 2009.06419.

[17] Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, et al. Advances and open problems in federated learning. 2019. arXiv: 1912.04977.

[18] Samarakoon S, Bennis M, Saad W, Debbah M. Distributed federated learning for ultra-reliable low-latency vehicular communications. IEEE Trans Commun 2020;68(2):1146–59. 链接1

[19] Kang J, Xiong Z, Niyato D, Xie S, Zhang J. Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J 2019;6(6):10700–14. 链接1

[20] Yang Z, Chen M, Saad W, Hong CS, Shikh-Bahaei M. Energy efficient federated learning over wireless communication networks. IEEE Trans Wirel Commun 2021;20(3):1935–49. 链接1

[21] Chen M, Poor HV, Saad W, Cui S. Wireless communications for collaborative federated learning. IEEE Commun Mag 2020;58(12):48–54. 链接1

[22] Kang J, Xiong Z, Niyato D, Zou Y, Zhang Y, Guizani M. Reliable federated learning for mobile networks. IEEE Wirel Commun 2020;27(2):72–80. 链接1

[23] Liu B, Wang L, Liu M, Xu C. Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems. 2019. arXiv: 1901.06455.

[24] Wang X, Wang C, Li X, Leung VCM, Taleb T. Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J 2020;7(10):9441–55. 链接1

[25] Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 2020;37(3):50–60. 链接1

[26] Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang YC, Yang Q, et al. Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun Surv Tutor 2020;22(3):2031–63.

[27] Murshed MG, Murphy C, Hou D, Khan N, Ananthanarayanan G, Hussain F. Machine learning at the network edge: a survey. 2019. arXiv: 1908.00080. 链接1

[28] Wang X, Han Y, Wang C, Zhao Q, Chen X, Chen M. In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw 2019;33(5):156–65. 链接1

[29] Park J, Samarakoon S, Bennis M, Debbah M. Wireless network intelligence at the edge. Proc IEEE 2019;107(11):2204–39. 链接1

[30] Aledhari M, Razzak R, Parizi RM, Saeed F. Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 2020;8:140699–725. 链接1

[31] Sun Y, Shi W, Huang X, Zhou S, Niu Z. Edge learning with timeliness constraints: challenges and solutions. IEEE Commun Mag 2020;58(12):27–33. 链接1

[32] Wei X, Shen C. Federated learning over noisy channels: convergence analysis and design examples. 2021. arXiv: 2101.02198.

[33] Zheng S, Shen C, Chen X. Design and analysis of uplink and downlink communications for federated learning. IEEE J Sel Areas Commun 2021;39 (7):2150–67. 链接1

[34] Yang K, Jiang T, Shi Y, Ding Z. Federated learning via over-the-air computation. IEEE Trans Wirel Commun 2020;19(3):2022–35. 链接1

[35] Xu C, Liu S, Yang Z, Huang Y, Wong KK. Learning rate optimization for federated learning exploiting over- the-air computation. IEEE J Sel Areas Commun. In press.

[36] Shi W, Zhou S, Niu Z, Jiang M, Geng L. Joint device scheduling and resource allocation for latency constrained wireless federated learning. IEEE Trans Wirel Commun 2021;20(1):453–67. 链接1

[37] Amiri MM, Gunduz D, Kulkarni SR, Poor HV. Convergence of update aware device scheduling for federated learning at the wireless edge. IEEE Trans Wirel Commun 2021;20(6):3643–58. 链接1

[38] Park J, Samarakoon S, Shiri H, Abdel-Aziz MK, Nishio T, Elgabli A, et al. Extreme URLLC: vision, challenges, and key enablers. 2020. arXiv: 2001.09683.

[39] Li Z, Uusitalo MA, Shariatmadari H, Singh B. 5G URLLC: design challenges and system concepts. In: Proceedings of 15th International Symposium on Wireless Communication Systems; 2018 Aug 28–31; Lisbon, Portugal; 2018.

[40] Basar E, Di Renzo M, De Rosny J, Debbah M, Alouini MS, Zhang R. Wireless communications through reconfigurable intelligent surfaces. IEEE Access 2019;7:116753–73. 链接1

[41] Zhang S, Zhang R. Capacity characterization for intelligent reflecting surface aided MIMO communication. IEEE J Sel Areas Commun 2020;38(8):1823–38. 链接1

[42] Hu S, Chitti K, Rusek F, Edfors O. User assignment with distributed large intelligent surface (LIS) systems. In: Proceedings of 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications; 2018 Sep 9–12; Bologna, Italy; 2018.

[43] Pan C, Ren H, Wang K, Xu W, Elkashlan M, Nallanathan A, et al. Intelligent reflecting surface for multicell MIMO communications. 2019. arXiv: 1907.10864.

[44] Nadeem QUA, Kammoun A, Chaaban A, Debbah M, Alouini MS. Asymptotic analysis of large intelligent surface assisted MIMO communication. 2019. arXiv: 1903.08127.

[45] Wei L, Huang C, Alexandropoulos GC, Yang Z, Yuen C, Zhang Z. Joint channel estimation and signal recovery in RIS-assisted multi-user MISO communications. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC); 2021 Mar 29–Apr 1; Nanjing, China; 2021.

[46] Huang C, Zappone A, Alexandropoulos GC, Debbah M, Yuen C. Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans Wirel Commun 2019;18(8):4157–70. 链接1

[47] Huang C, Mo R, Yuen C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning. IEEE J Sel Areas Commun 2020;38(8):1839–50. 链接1

[48] Huang C, Hu S, Alexandropoulos GC, Zappone A, Yuen C, Zhang R, et al. Holographic MIMO surfaces for 6G wireless networks: opportunities, challenges, and trends. 2019. arXiv: 1911.12296.

[49] Yu X, Xu D, Sun Y, Ng DWK, Schober R. Robust and secure wireless communications via intelligent reflecting surfaces. IEEE J Sel Areas Commun 2020;38(11):2637–52. 链接1

[50] Zheng B, You C, Zhang R. Double-IRS assisted multi-user MIMO: cooperative passive beamforming design. IEEE Trans Wirel Commun 2021;20(7):4513–26. 链接1

[51] Chaccour C, Soorki MN, Saad W, Bennis M, Popovski P. Risk-based optimization of virtual reality over terahertz reconfigurable intelligent surfaces. 2020. arXiv: 2002.09052.

[52] Hum SV, Perruisseau-Carrier J. Reconfigurable reflect arrays and array lenses for dynamic antenna beam control: a review. IEEE Trans Antenn Propag 2014;62(1):183–98. 链接1

[53] Huang J, Li Q, Zhang Q, Zhang G, Qin J. Relay beamforming for amplify-andforward multi-antenna relay networks with energy harvesting constraint. IEEE Signal Process Lett 2014;21(4):454–8. 链接1

[54] Di Renzo M, Ntontin K, Song J, Danufane FH, Qian X, Lazarakis F, et al. Reconfigurable intelligent surfaces vs. relaying: differences, similarities, and performance comparison. IEEE Open J Commun Soc 2020;1:798. 链接1

[55] Elbir AM, Coleri S. Federated learning for channel estimation in conventional and IRS-assisted massive MIMO. 2020. arXiv: 2008.10846.

[56] Yang K, Shi Y, Zhou Y, Yang Z, Fu L, Chen W. Federated machine learning for intelligent IoT via reconfigurable intelligent surface. IEEE Network 2020;34 (5):16–22. 链接1

[57] Ni W, Liu Y, Yang Z, Tian H, Shen X. Federated learning in multi-RIS aided systems. 2020. arXiv: 2010.13333.

[58] Huang C, Alexandropoulos GC, Yuen C, Debbah M. Indoor signal focusing with deep learning designed reconfigurable intelligent surfaces. In: Proceedings of 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); 2019 Jul 2–5; Cannes, France; 2019.

[59] Huang C, Yang Z, Alexandropoulos GC, Xiong K, Wei L, Yuen C, et al. Hybrid beamforming for RIS-empowered multi-hop terahertz communications: a DRL-based method. 2020. arXiv: 2009.09380.

[60] Xie H, Qin Z, Li GY, Juang BH. Deep learning enabled semantic communication systems. IEEE Trans Signal Process 2021;69:2663–75. 链接1

[61] Xie H, Qin Z. A lite distributed semantic communication system for Internet of Things. 2020. arXiv: 2007.11095.

[62] Chen M, Semiari O, Saad W, Liu X, Yin C. Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks. IEEE Trans Wirel Commun 2020;19(1):177–91. 链接1

[63] Chen M, Challita U, Saad W, Yin C, Debbah M. Artificial neural networks-based machine learning for wireless networks: a tutorial. IEEE Commun Surv Tutor 2019;21(4):3039–71. 链接1

[64] Ding Z, Liu Y, Choi J, Sun Qi, Elkashlan M, Chih-Lin I, et al. Application of nonorthogonal multiple access in LTE and 5G networks. IEEE Commun Mag 2017;55(2):185–91. 链接1

[65] Liu Y, Qin Z, Elkashlan M, Ding Z, Nallanathan A, Hanzo L. Nonorthogonal multiple access for 5G and beyond. Proc IEEE 2017;105(12):2347–81. 链接1

[66] Liu Y, Xing H, Pan C, Nallanathan A, Elkashlan M, Hanzo L. Multiple-antennaassisted non-orthogonal multiple access. IEEE Wirel Commun 2018;25 (2):17–23. 链接1

[67] Qin Z, Yue X, Liu Y, Ding Z, Nallanathan A. User association and resource allocation in unified NOMA enabled heterogeneous ultra dense networks. IEEE Commun Mag 2018;56(6):86–92. 链接1

[68] Yang Z, Xu W, Pan C, Pan Y, Chen M. On the optimality of power allocation for NOMA downlinks with individual QoS constraints. IEEE Commun Lett 2017;21 (7):1649–52. 链接1

[69] Yang Z, Pan C, Xu W, Pan Y, Chen M, Elkashlan M. Power control for multi-cell networks with non-orthogonal multiple access. IEEE Trans Wirel Commun 2018;17(2):927–42. 链接1

[70] Shin W, Vaezi M, Lee B, Love DJ, Lee J, Poor HV. Non-orthogonal multiple access in multi-cell networks: theory, performance, and practical challenges. IEEE Commun Mag 2017;55(10):176–83. 链接1

[71] Ni W, Liu X, Liu Y, Tian H, Chen Y. Resource allocation for multi-cell IRS-aided NOMA networks. IEEE Trans Wirel Commun 2021;20(7):4253–68. 链接1

[72] Andrieu C, de Freitas N, Doucet A, Jordan MI. An introduction to MCMC for machine learning. Mach Learn 2003;50(1–2):5–43. 链接1

[73] Freeman WT, Pasztor EC, Carmichael OT. Learning low-level vision. Int J Comput Vis 2000;40(1):25–47. 链接1

[74] Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of 25th International Conference on Machine Learning; 2008 Jul 5–9; Helsinki, Finland; 2008.

[75] Bishop CM. Pattern recognition and machine learning. Berlin: Springer; 2006. 链接1

[76] Lee H, Wicke M, Kusy B, Gnawali O, Guibas L. Predictive data delivery to mobile users through mobility learning in wireless sensor networks. IEEE Trans Veh Technol 2015;64(12):5831–49. 链接1

[77] Yao L, Chen A, Deng J, Wang J, Wu G. A cooperative caching scheme based on mobility prediction in vehicular content centric networks. IEEE Trans Veh Technol 2018;67(6):5435–44. 链接1

[78] Chen M, Mozaffari M, Saad W, Yin C, Debbah M, Hong CS. Caching in the sky: proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience. IEEE J Sel Areas Commun 2017;35 (5):1046–61. 链接1

[79] Yin J, Li L, Zhang H, Li X, Gao A, Han Z. A prediction-based coordination caching scheme for content centric networking. In: Proceedings of 27th Wireless and Optical Communication Conference; 2018 Apr 30–May 1; Hualien, China; 2018.

[80] Yang Z, Xu W, Xu H, Shi J, Chen M. Energy efficient non-orthogonal multiple access for machine-to-machine communications. IEEE Commun Lett 2017;21 (4):817–20. 链接1

[81] Yang Z, Xu W, Pan Y, Pan C, Chen M. Energy efficient resource allocation in machine-to-machine communications with multiple access and energy harvesting for IoT. IEEE Internet Things J 2018;5(1):229–45. 链接1

[82] Cui J, Ding Z, Fan P, Al-Dhahir N. Unsupervised machine learning-based user clustering in millimeter-wave-NOMA systems. IEEE Trans Wirel Commun 2018;17(11):7425–40. 链接1

[83] Li M, Zhou L, Yang Z, Li A, Xia F, Andersen DG, et al. Parameter server for distributed machine learning. In: Proceedings of Big Learning NIPS Workshop; 2013 Dec 5–10; Harrahs and Harveys, NV, USA, 2013. 链接1

[84] Bekkerman R, Bilenko M, Langford J. Scaling up machine learning: parallel and distributed approaches. Cambridge: Cambridge University Press; 2011. 链接1

[85] Kim M, Kim NI, Lee W, Cho DH. Deep learning-aided SCMA. IEEE Commun Lett 2018;22(4):720–3. 链接1

[86] Samarakoon S, Bennis M, Saad W, Debbah M. Federated learning for ultrareliable low-latency V2V communications. In: Proceedings of IEEE Global Communications Conference; 2018 Dec 9–13; Abu Dhabi, United Arab Emirates; 2018.

[87] Ma C, Li J, Ding M, Yang HH, Shu F, Quek TQ, et al. On safeguarding privacy and security in the framework of federated learning. IEEE Netw 2020;34(4):242–8. 链接1

[88] Li X, Huang K, Yang W, Wang S, Zhang Z. On the convergence of FedAvg on non-IID data. 2020. arXiv: 1907.02189.

[89] Luo S, Chen X, Wu Q, Zhou Z, Yu S. HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Trans Wirel Commun 2020;19(10):6535–48. 链接1

[90] Chen M, Poor HV, Saad W, Cui S. Convergence time optimization for federated learning over wireless networks. IEEE Trans Wirel Commun 2021;20 (4):2457–71. 链接1

[91] Yang HH, Liu Z, Quek TQS, Poor HV. Scheduling policies for federated learning in wireless networks. IEEE Trans Commun 2020;68(1):317–33. 链接1

[92] Khaled A, Mishchenko K, Richtárik P. Tighter theory for local SGD on identical and heterogeneous data. In: Proceedings of International Conference on Artificial Intelligence and Statistics; 2020 Aug 26–28; online; 2020.

[93] Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, et al. Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans Inf Forensics Secur 2020;15:3454–69. 链接1

[94] Chen M, Shlezinger N, Poor HV, Eldar YC, Cui S. Communication efficient federated learning. Proc Natl Acad Sci 2021; 118(17): e2017318118.

[95] Chen M, Gunduz D, Huang K, Saad W, Bennis M, Feljan AV, et al. Distributed learning in wireless networks: recent progress and future challenges. IEEE J Sel Areas Commun 2021;39(12):3579–605. 链接1

[96] Tian Y, Zhang Z, Yang Z, Yang Q. JMSNAS: joint model split and neural architecture search for learning over mobile edge networks. 2021. arXiv: 2111.08206.

[97] Tong X, Zhang Z, Wang J, Huang C, Debbah M. Joint multi-user communication and sensing exploiting both signal and environment sparsity. IEEE J Sel Topics Signal Process. In press.

[98] Yang Y, Zhang Z, Yang Q. Communication-efficient federated learning with binary neural networks. IEEE J Sel Areas Commun. In press.

[99] Qi Q, Chen X, Zhong C, Zhang Z. Integrated sensing, computation and communication in B5G cellular Internet of Things. IEEE Trans Wirel Commun 2021;20(1):332–44. 链接1

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