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This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machine learning (ML) model using the rich data and computational resources of mobile clients without collecting their data in central systems.
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Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep gradient compression: Reducing the communication bandwidth for distributed training,” in Proc. ICLR , Vancouver, BC, Canada, May 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Yoshida, T. Nishio, M. Morikura, Y. Koji, and R. Yonetani, “Hybrid-FL for wireless networks: cooperative learning mechanism using non-IID data,” in Proc. IEEE ICC , Jun. 2020, pp. 1–7
2020
Closest in time.
H. H. Yang, A. Arafa, T. Q. S. Quek, and H. Vincent Poor, “Age-based scheduling policy for federated learning in mobile edge networks,” in Proc. IEEE ICASSP , May 2020, pp. 8743–8747
2020
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2020
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F. Li, D. Yu, H. Yang, J. Yu, H. Karl, and X. Cheng, “Multi-armed-bandit-based spectrum scheduling algorithms in wireless networks: A survey,” IEEE Wireless Commun. , vol. 27, no. 1, pp. 24–30, Feb. 2020
2020
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2019
Cited alongside, same era.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in Proc. IEEE ICC , May 2019, pp. 1–7
2019
Cited alongside, same era.
P. Zhao, H. Tian, K. Chen, S. Fan, and G. Nie, “Context-aware tdd configuration and resource allocation for mobile edge computing,” IEEE Trans. Commun. , vol. 68, no. 2, pp. 1118–1131, 2020
2020
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