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Energy-Efficient Radio Resource Allocation for Federated Edge Learning · Around

2019

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Zeng, Qunsong, Du, Yuqing, Leung, Kin K. et al.

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Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources.

Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation.
FEEL coordinates global model training at a server and local model training at edge devices over wireless links.
In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL.

Built on

M. M. Amiri and D. Gündüz, “Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air.” [Online]. Available: http://arxiv.org/abs/1901.00844
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1901
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. of the 20th Intel. Conf. Artificial Intell. and Statistics , vol. 54, pp. 1273–1282, Fort Lauderdale, FL, USA, Apr 20–22 2017
2017
Earlier work this paper cites.

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X. Mei, Q. Wang, and X. Chu, “A survey and measurement study of GPU DVFS on energy conservation,” Digital Comm. and Networks , vol. 3, no. 2, pp. 89–100, 2017
2017
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S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” in IEEE Conf. Computer Comm., INFOCOM , pp. 63–71, Honolulu, HI, USA, Apr 16–19 2018
2018
Cited alongside, same era.
G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Towards an intelligent edge: Wireless communication meets machine learning.” [Online]. Available: http://arxiv.org/abs/1809.00343
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Cited in the paper.
G. Zhu, Y. Wang, and K. Huang, “Low-latency broadband analog aggregation for federated edge learning.” [Online]. Available: http://arxiv.org/abs/1812.11494
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Cited in the paper.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge.” [Online]. Available: http://arxiv.org/abs/1804.08333
Original
Cited in the paper.
K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation.” [Online]. Available: http://arxiv.org/abs/1812.11750
Original
Cited in the paper.

Then

J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar, “signSGD: Compressed optimisation for non-convex problems,” in Proc. of the 35th Intl. Conf. Mach. Learning (ICML) , vol. 80, pp. 560–569, Stockholmsmässan, Stockholm Sweden, Jul 10–15 2018
2018
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