Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

M. Goldenbaum and S. Stanczak, “Robust analog function computation via wireless multiple-access channels,” IEEE Transactions on Communications , vol. 61, no. 9, pp. 3863–3877, 2013

2013

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385 , 2015

Original

2015

Earlier work this paper cites.

O. Abari, H. Rahul, and D. Katabi, “Over-the-air function computation in sensor networks,” arXiv preprint arXiv:1612.02307 , 2016

Original

2016

Earlier work this paper cites.

J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016

Original

2016

Earlier work this paper cites.

D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic, “Qsgd: Communication-efficient sgd via gradient quantization and encoding,” in Advances in Neural Information Processing Systems , 2017, pp. 1709–1720

2017

Earlier work this paper cites.

W. Wen, C. Xu, F. Yan, C. Wu, Y. Wang, Y. Chen, and H. Li, “Terngrad: Ternary gradients to reduce communication in distributed deep learning,” in Advances in Neural Information Processing Systems , 2017, pp. 1509–1519

2017

Earlier work this paper cites.

A. F. Aji and K. Heafield, “Sparse communication for distributed gradient descent,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 440–445

2017

Earlier work this paper cites.

G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Towards an intelligent edge: Wireless communication meets machine learning,” arXiv preprint arXiv:1809.00343 , 2018

Original

2018

Earlier work this paper cites.

J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar, “Signsgd: Compressed optimisation for non-convex problems,” in International Conference on Machine Learning , 2018, pp. 559–568

2018

Earlier work this paper cites.

J. Wu, W. Huang, J. Huang, and T. Zhang, “Error compensated quantized sgd and its applications to large-scale distributed optimization,” in International Conference on Machine Learning , 2018, pp. 5321–5329

2018

Earlier work this paper cites.

S. U. Stich, J.-B. Cordonnier, and M. Jaggi, “Sparsified sgd with memory,” in Advances in Neural Information Processing Systems , 2018, pp. 4447–4458

2018

Cited alongside, same era.

D. Alistarh, T. Hoefler, M. Johansson, N. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems , 2018, pp. 5973–5983

2018

Cited alongside, same era.

J. Dong, Y. Shi, and Z. Ding, “Blind over-the-air computation and data fusion via provable wirtinger flow,” arXiv preprint arXiv:1811.04644 , 2018

Original

2018

Cited alongside, same era.

G. Zhu and K. Huang, “MIMO over-the-air computation for high-mobility multi-modal sensing,” IEEE Internet of Things Journal , 2018

2018

Cited alongside, same era.

K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” arXiv preprint arXiv:1812.11750 , 2018

Q. Zeng, Y. Du, K. K. Leung, and K. Huang, “Energy-efficient radio resource allocation for federated edge learning,” arXiv preprint arXiv:1907.06040 , 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,” arXiv preprint arXiv:1901.00844 , 2019

Original

2019

Later among the works it cites.

——, “Federated learning over wireless fading channels,” arXiv preprint arXiv:1907.09769 , 2019

Original

2019

Later among the works it cites.

——, “Over-the-air machine learning at the wireless edge,” in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . IEEE, 2019, pp. 1–5

2019

Later among the works it cites.