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In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
Low-latency broadband analog aggregation for federated edge learning
Zhu, G., Wang, Y., and Huang, K · 2007
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Feedback prediction for blogs
Buza, K · 2014
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Konečnỳ, J., McMahan, H. B., Ramage, D., and Richtárik, P · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y · 2016
Earlier work this paper cites.
Distributed federated learning for ultra-reliable low-latency vehicular communications
Samarakoon, S., Bennis, M., Saad, W., and Debbah, M · 2018
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When edge meets learning: Adaptive control for resource-constrained distributed machine learning
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., and Chan, K · 2018
Earlier work this paper cites.
Federated learning via over-the-air computation
Yang, K., Jiang, T., Shi, Y., and Ding, Z · 2018
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Wireless federated distillation for distributed edge learning with heterogeneous data
Ahn, J.-H., Simeone, O., and Kang, J · 2019
Earlier work this paper cites.
Artificial neural networks-based machine learning for wireless networks: A tutorial
Chen, M., Challita, U., Saad, W., Yin, C., and Debbah, M · 2019
Cited alongside, same era.
A joint learning and communications framework for federated learning over wireless networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., and Cui, S · 2019
Cited alongside, same era.
Deep CNN-based channel estimation for mmWave massive MIMO systems
Dong, P., Zhang, H., Li, G. Y., Gaspar, I. S., and NaderiAlizadeh, N · 2019
Cited alongside, same era.
Machine learning in the air
Gündüz, D., de Kerret, P., Sidiropoulos, N. D., Gesbert, D., Murthy, C. R., and van der Schaar, M · 2019
Cited alongside, same era.
Wireless network intelligence at the edge
Park, J., Samarakoon, S., Bennis, M., and Debbah, M · 2019
Cited alongside, same era.
Energy-efficient radio resource allocation for federated edge learning
Zeng, Q., Du, Y., Leung, K. K., and Huang, K · 2019
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Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks
Chen, M., Semiari, O., Saad, W., Liu, X., and Yin, C · 2020
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Reinforcement learning based cooperative coded caching under dynamic popularities in ultra-dense networks
Gao, S., Dong, P., Pan, Z., and Li, G. Y · 2020
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Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning
Huang, C., Mo, R., and Yuen, C · 2020
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A vision of 6G wireless systems: Applications, trends, technologies, and open research problems
Saad, W., Bennis, M., and Chen, M · 2020
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Tran, N. H., Bao, W., Zomaya, A., and Hong, C. S · 2019
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., and Chan, K · 2019
Cited alongside, same era.
Energy efficient federated learning over wireless communication networks
Yang, Z., Chen, M., Saad, W., Hong, C. S., and Shikh-Bahaei, M · 2019
Cited alongside, same era.
Performance optimization of federated learning over wireless networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., and Cui, S
Cited in the paper.
Towards an intelligent edge: Wireless communication meets machine learning
Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., and Huang, K
Cited in the paper.
Scheduling policies for federated learning in wireless networks
Yang, H. H., Liu, Z., Quek, T. Q. S., and Poor, H. V · 2020
Closest in time.
Energy-efficient wireless communications with distributed reconfigurable intelligent surfaces, 2020
Yang, Z., Chen, M., Saad, W., Xu, W., Shikh-Bahaei, M., Poor, H. V., and Cui, S · 2020
Closest in time.