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2020

Adaptive Federated Learning With Gradient Compression in Uplink NOMA

Sun, Haijian, Ma, Xiang, Hu, Rose Qingyang

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Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices.

Several unique features such as energy saving and privacy preserving make FL a highly promising learning approach for power-limited and privacy sensitive devices.
Although distributed computing can lower down the information amount that needs to be uploaded, model updates in FL can still experience performance bottleneck, especially for updates via wireless connections.
In this work, we investigate the performance of FL update with mobile edge devices that are connected to the parameter server (PS) with practical wireless links, where uplink update from user to PS has very limited capacity.

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Original
2003
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I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”, MIT Press , 2016
2016
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N. Zhang, J. Wang, G. Kang, and Y. Liu, “Uplink nonorthogonal multiple access in 5G systems”, in IEEE Commun. Lett. , vol. 20, no. 3, pp. 458-461, Mar. 2016
2016
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H. B. McManhan, E. Moore, D. Ramage, S. Hampson, and B. A. Arcas, “Communication-efficient learning of deep networks from decentralized data”, in Proc. 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, 2017
2017
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Similar

Z. Zhang, H. Sun and R. Q. Hu, “Downlink and Uplink Non-Orthogonal Multiple Access in a Dense Wireless Network,” in IEEE J. Sel. Area Comm. , vol. 35, no. 12, pp. 2771-2784, Dec. 2017
2017
Cited alongside, same era.
L. T. Tan and R. Q. Hu, “Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning”, in IEEE Trans. Veh. Technol. , vol. 67, no. 11, pp. 10190-10203, Nov. 2018
2018
Cited alongside, same era.
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep gradient compression: reducing the communication bandwidth for distributed training”, in ICLR , 2018
2018
Cited alongside, same era.

Then

S. Zhou , Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou,“Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,” [Online]: https://arxiv.org/abs/1606.06160, 2018
Original
2018
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks”, [Online]: https://arxiv.org/abs/1812.06127, 2019
Original
2019
Later among the works it cites.
M. M. Amiri and D. Gündüz, “Machine learning at the wireless edge: distributed stochastic gradient descent over-the-air”, in IEEE International Symposium on Information Theory, Paris, France, Jul. 2019
2019
Later among the works it cites.

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