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In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied.
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Convex Optimization
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J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, · 2016
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“Federated learning: Strategies for improving communication efficiency,”
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“Resource allocation for D2D communications underlaying a NOMA-based cellular network,”
Y. Pan, C. Pan, Z. Yang, and M. Chen, · 2018
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“Performance optimization of federated learning over wireless networks,”
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, · 2019
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“Artificial neural networks-based machine learning for wireless networks: A tutorial,”
M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, · 2019
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“Application of machine learning in wireless networks: Key techniques and open issues,”
Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, · 2019
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“Towards federated learning at scale: System design,”
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. M. Kiddon, J. Konecny, S. Mazzocchi, B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander, · 2019
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“In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,”
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, · 2019
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“Engineering radio maps for wireless resource management,”
S. Bi, J. Lyu, Z. Ding, and R. Zhang, · 2019
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“When machine learning meets big data: A wireless communication perspective,”
Y. Liu, S. Bi, Z. Shi, and L. Hanzo, · 2020
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“A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,”
W. Saad, M. Bennis, and M. Chen, · 2020
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“Federated learning: Challenges, methods, and future directions,”
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, · 2020
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“Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks,”
M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin, · 2020
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“Distributed federated learning for ultra-reliable low-latency vehicular communications,”
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“Multi-hop federated private data augmentation with sample compression,”
E. Jeong, S. Oh, J. Park, H. Kim, M. Bennis, and S. L. Kim, · 2019
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“Energy efficient federated learning over wireless communication networks,”
Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, · 2019
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“Coded federated computing in wireless networks with straggling devices and imperfect CSI,”
S. Ha, J. Zhang, O. Simeone, and J. Kang, · 2019
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“Fast uplink grant for NOMA: A federated learning based approach,”
O. Habachi, M. A. Adjif, and J. P. Cances, · 2019
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“Wireless network intelligence at the edge,”
J. Park, S. Samarakoon, M. Bennis, and M. Debbah, · 2019
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“Adaptive federated learning in resource constrained edge computing systems,”
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, · 2019
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“Federated learning over wireless networks: Optimization model design and analysis,”
N. H. Tran, W. Bao, A. Zomaya, N. Minh N.H., and C. S. Hong, · 2019
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S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, · 2020
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“Energy-efficient radio resource allocation for federated edge learning,”
Q. Zeng, Y. Du, K. Huang, and K. K. Leung, · 2020
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“Federated-learning-enabled intelligent fog radio access networks: Fundamental theory, key techniques, and future trends,”
Z. Zhao, C. Feng, H. H. Yang, and X. Luo, · 2020
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“Cell-free massive MIMO for wireless federated learning,”
T. T. Vu, D. T. Ngo, N. H. Tran, H. Q. Ngo, M. N. Dao, and R. H. Middleton, · 2020
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“Convergence time optimization for federated learning over wireless networks,”
M. Chen, H. V. Poor, W. Saad, and S. Cui, · 2020
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“Scheduling policies for federated learning in wireless networks,”
H. H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, · 2020
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