PDF(978 KB)
Federated Learning for 6G: Applications, Challenges, and Opportunities
Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, Shuguang Cui
Engineering ›› 2022, Vol. 8 ›› Issue (1) : 33-41.
PDF(978 KB)
PDF(978 KB)
Federated Learning for 6G: Applications, Challenges, and Opportunities
Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.
Federated learning 6G / Reconfigurable intelligent surface / Semantic communication / Sensing / communication and computing
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [10] |
Amiri MM, Gunduz D. Federated learning over wireless fading channels. IEEE Trans Wirel Commun 2020;19(5):3546–57.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [21] |
Chen M, Poor HV, Saad W, Cui S. Wireless communications for collaborative federated learning. IEEE Commun Mag 2020;58(12):48–54.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [29] |
Park J, Samarakoon S, Bennis M, Debbah M. Wireless network intelligence at the edge. Proc IEEE 2019;107(11):2204–39.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [41] |
Zhang S, Zhang R. Capacity characterization for intelligent reflecting surface aided MIMO communication. IEEE J Sel Areas Commun 2020;38(8):1823–38.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [73] |
Freeman WT, Pasztor EC, Carmichael OT. Learning low-level vision. Int J Comput Vis 2000;40(1):25–47.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [84] |
Bekkerman R, Bilenko M, Langford J. Scaling up machine learning: parallel and distributed approaches. Cambridge: Cambridge University Press; 2011.
|
| [85] |
Kim M, Kim NI, Lee W, Cho DH. Deep learning-aided SCMA. IEEE Commun Lett 2018;22(4):720–3.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
| [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.
|
/
| 〈 |
|
〉 |
AI Summary
