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Traditional machine learning is centralized in the cloud (data centers).
W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, “Learning low-level vision,” International Journal of Computer Vision , vol. 40, no. 1, pp. 25–47, Oct. 2000
2000
Earlier work this paper cites.
C. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan, “An introduction to MCMC for machine learning,” Machine Learning , vol. 50, no. 1-2, pp. 5–43, Jan. 2003
2003
Earlier work this paper cites.
C. M. Bishop, Pattern recognition and machine learning . Springer, 2006
2006
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proc. of the International Conference on Machine Learning , New York, NY, USA, July 2008, pp. 160–167
2008
Earlier work this paper cites.
R. Bekkerman, M. Bilenko, and J. Langford, Scaling up machine learning: Parallel and distributed approaches . Cambridge University Press, 2011
2011
Earlier work this paper cites.
S. V. Hum and J. Perruisseau-Carrier, “Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: A review,” IEEE Trans. Antennas Prop. , vol. 62, no. 1, pp. 183–198, Jan. 2013
2013
Earlier work this paper cites.
M. Li, L. Zhou, Z. Yang, A. Li, F. Xia, D. G. Andersen, and A. Smola, “Parameter server for distributed machine learning,” in Big Learning NIPS Workshop , vol. 6, 2013, p. 2
2013
Earlier work this paper cites.
J. Huang, Q. Li, Q. Zhang, G. Zhang, and J. Qin, “Relay beamforming for amplify-and-forward multi-antenna relay networks with energy harvesting constraint,” IEEE Signal Process. Lett. , vol. 21, no. 4, pp. 454–458, Apr. 2014
2014
Earlier work this paper cites.
H. Lee, M. Wicke, B. Kusy, O. Gnawali, and L. Guibas, “Predictive data delivery to mobile users through mobility learning in wireless sensor networks,” IEEE Trans. Veh. Technol. , vol. 64, no. 12, pp. 5831–5849, Dec 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Z. Ding, Y. Liu, J. Choi, Q. Sun, M. Elkashlan, C.-L. I, and H. V. Poor, “Application of non-orthogonal multiple access in LTE and 5G networks,” IEEE Commun. Mag. , vol. 55, no. 2, pp. 185–191, Feb. 2017
2017
Earlier work this paper cites.
Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Nonorthogonal multiple access for 5G and beyond,” Proceedings of the IEEE , vol. 105, no. 12, pp. 2347–2381, Dec. 2017
2017
Earlier work this paper cites.
Z. Yang, W. Xu, C. Pan, Y. Pan, and M. Chen, “On the optimality of power allocation for NOMA downlinks with individual QoS constraints,” IEEE Commun. Lett. , vol. 21, no. 7, pp. 1649–1652, July 2017
2017
Earlier work this paper cites.
W. Shin, M. Vaezi, B. Lee, D. J. Love, J. Lee, and H. V. Poor, “Non-orthogonal multiple access in multi-cell networks: Theory, performance, and practical challenges,” IEEE Commun. Mag. , vol. 55, no. 10, pp. 176–183, Oct. 2017
2017
Earlier work this paper cites.
M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, “Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,” IEEE J. Sel. Areas Commun. , vol. 35, no. 5, pp. 1046–1061, May 2017
2017
Earlier work this paper cites.
Z. Yang, W. Xu, H. Xu, J. Shi, and M. Chen, “Energy efficient non-orthogonal multiple access for machine-to-machine communications,” IEEE Commun. Lett. , vol. 21, no. 4, pp. 817–820, Apr. 2017
2017
Earlier work this paper cites.
S. Hu, K. Chitti, F. Rusek, and O. Edfors, “User assignment with distributed large intelligent surface (LIS) systems,” in Proc. IEEE Int. Symposium Personal, Indoor Mobile Radio Commun. , Bologna, Italy, Sep. 2018, pp. 1–6
2018
Earlier work this paper cites.
Y. Liu, H. Xing, C. Pan, A. Nallanathan, M. Elkashlan, and L. Hanzo, “Multiple-antenna-assisted non-orthogonal multiple access,” IEEE Wireless Commun. , vol. 25, no. 2, pp. 17–23, Apr. 2018
2018
Earlier work this paper cites.
Z. Qin, X. Yue, Y. Liu, Z. Ding, and A. Nallanathan, “User association and resource allocation in unified NOMA enabled heterogeneous ultra dense networks,” IEEE Commun. Mag. , vol. 56, no. 6, pp. 86–92, June 2018
2018
Earlier work this paper cites.
Z. Yang, C. Pan, W. Xu, Y. Pan, M. Chen, and M. Elkashlan, “Power control for multi-cell networks with non-orthogonal multiple access,” IEEE Trans. Wireless Commun. , vol. 17, no. 2, pp. 927–942, Feb. 2018
2018
Earlier work this paper cites.
L. Yao, A. Chen, J. Deng, J. Wang, and G. Wu, “A cooperative caching scheme based on mobility prediction in vehicular content centric networks,” IEEE Trans. Veh. Technol. , vol. 67, no. 6, pp. 5435–5444, June 2018
2018
Earlier work this paper cites.
J. Yin, L. Li, H. Zhang, X. Li, A. Gao, and Z. Han, “A prediction-based coordination caching scheme for content centric networking,” in Proc. of Wireless and Optical Communication Conference , Hualien, Taiwan, April 2018
2018
Earlier work this paper cites.
Z. Yang, W. Xu, Y. Pan, C. Pan, and M. Chen, “Energy efficient resource allocation in machine-to-machine communications with multiple access and energy harvesting for IoT,” IEEE Internet Things J. , vol. 5, no. 1, pp. 229–245, Feb. 2018
2018
Earlier work this paper cites.
J. Cui, Z. Ding, P. Fan, and N. Al-Dhahir, “Unsupervised machine learning-based user clustering in millimeter-wave-NOMA systems,” IEEE Trans. Wireless Commun. , vol. 17, no. 11, pp. 7425–7440, Nov. 2018
2018
Earlier work this paper cites.
M. Kim, N.-I. Kim, W. Lee, and D.-H. Cho, “Deep learning-aided SCMA,” IEEE Commun. Lett. , vol. 22, no. 4, pp. 720–723, 2018
2018
Earlier work this paper cites.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency V2V communications,” in Proc. IEEE Global Commun. Conf. , Abu Dhabi, United Arab Emirates, Dec. 2018, pp. 1–7
2018
Earlier work this paper cites.
G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Trans. Wireless Commun. , vol. 19, no. 1, pp. 491–506, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications , vol. 68, no. 2, pp. 1146–1159, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
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M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Trans. Wireless Commun. , vol. 19, no. 5, pp. 3546–3557, 2020
2020
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2020
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2020
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2019
Cited alongside, same era.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Network , vol. 33, no. 5, pp. 156–165, 2019
2019
Cited alongside, same era.
J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proceedings of the IEEE , vol. 107, no. 11, pp. 2204–2239, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Trans. Wireless Commun. , vol. 18, no. 8, pp. 4157–4170, Aug. 2019
2019
Cited alongside, same era.
2020
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2020
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2020
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Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy efficient federated learning over wireless communication networks,” IEEE Trans. Wireless Commun. , 2020 (To appear)
2020
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T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Later among the works it cites.
W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials , 2020
2020
Later among the works it cites.
M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated learning: A survey on enabling technologies, protocols, and applications,” IEEE Access , vol. 8, pp. 140 699–140 725, 2020
2020
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K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans. Wireless Commun. , pp. 1–1, 2020
2020
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2020
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C. Huang, R. Mo, and C. Yuen, “Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning,” IEEE J. Sel. Areas Commun. , vol. 38, pp. 1–1, 2020
2020
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2020
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2020
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M. Chen, H. V. Poor, W. Saad, and S. Cui, “Convergence time optimization for federated learning over wireless networks,” IEEE Trans. Wireless Commun. , 2020 (To appear)
2020
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