Fast Convergence Algorithm for Analog Federated Learning

S. Wang, K. Guan, D. He, G. Li, X. Lin, B. Ai, and Z. Zhong, “Doppler shift and coherence time of 5g vehicular channels at 3.5 ghz,” in 2018 IEEE International Symposium on Antennas and Propagation USNC/URSI National Radio Science Meeting , 2018, pp. 2005–2006

2006

Earlier work this paper cites.

B. Nazer and M. Gastpar, “Computation over multiple-access channels,” IEEE Transactions on Information Theory , vol. 53, no. 10, pp. 3498–3516, 2007

2007

Earlier work this paper cites.

H. Brendan McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” arXiv e-prints , p. arXiv:1602.05629, Feb. 2016

Original

2016

Earlier work this paper cites.

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated Optimization in Heterogeneous Networks,” arXiv e-prints , p. arXiv:1812.06127, Dec. 2018

Original

2018

Earlier work this paper cites.

S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications , vol. 37, no. 6, pp. 1205–1221, 2019

2019

Earlier work this paper cites.

Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol. , vol. 10, no. 2, p. 19, Jan. 2019. [Online]. Available: https://doi.org/10.1145/3298981

2019

Earlier work this paper cites.

S. Ravi, “Efficient on-device models using neural projections,” in International Conference on Machine Learning , 2019, pp. 5370–5379

2019

Earlier work this paper cites.

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” arXiv preprint arXiv:1907.02189 , 2019

Original

2019

Earlier work this paper cites.

T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020

2020

Cited alongside, same era.

W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y. C. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys Tutorials , vol. 22, no. 3, pp. 2031–2063, 2020

2020

Cited alongside, same era.

M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” IEEE Transactions on Wireless Communications , pp. 1–1, 2020

2020

Cited alongside, same era.

D. Wen, M. Bennis, and K. Huang, “Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning,” IEEE Transactions on Wireless Communications , pp. 1–1, 2020

2020

Cited alongside, same era.

G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Transactions on Wireless Communications , vol. 19, no. 1, pp. 491–506, 2020

2020

Closest in time.

M. Mohammadi Amiri and D. Gündüz, “Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air,” IEEE Transactions on Signal Processing , vol. 68, pp. 2155–2169, 2020

2020

Closest in time.

T. Sery and K. Cohen, “On analog gradient descent learning over multiple access fading channels,” IEEE Transactions on Signal Processing , vol. 68, pp. 2897–2911, 2020

2020

Closest in time.

R. Pathak and M. J. Wainwright, “FedSplit: An algorithmic framework for fast federated optimization,” arXiv e-prints , p. arXiv:2005.05238, May 2020

Original

2020

Closest in time.