Energy-Aware Analog Aggregation for Federated Learning with Redundant Data

M. J. Neely, Stochastic Network Optimization With Application to Communication and Queueing Systems . San Rafael, CA, USA: Morgan & Claypool, 2010

2010

Earlier work this paper cites.

J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” NIPS Workshop on Private Multi-Party Machine Learning , [Online] Available: https://arxiv.org/abs/1610.05492, Oct. 2016

Original

2016

Earlier work this paper cites.

Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts. , vol. 19, no. 4, pp. 2322-2358, 2017

2017

Earlier work this paper cites.

J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” [Online] Available: https://arxiv.org /abs/1812.02858, Dec. 2018

Original

2018

Earlier work this paper cites.

H. Li, K. Ota, and M. Dong, “Learning IoT in edge: Deep learning for the Internet of things with edge computing,” IEEE Network , vol. 32, no. 1, pp. 96-101, Jan.-Feb. 2018

2018

Earlier work this paper cites.

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” [Online] Available: https://arxiv.org/abs/1806.00582, Jun. 2018

Original

2018

Earlier work this paper cites.

S. Zhou, Y. Sun, Z. Jiang, and Z. Niu, “Exploiting moving intelligence: Delay-optimized computation offloading in vehicular fog networks,” IEEE Commun. Mag. , vol. 57, no. 5, pp. 49-55, May 2019

2019

Earlier work this paper cites.

K. Bonawitz, et al. “Towards federated learning at scale: System design,” In Conference on Systems and Machine Learning , Stanford, CA, USA, Apr. 2019

2019

Cited alongside, same era.

T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” [Online] Available: https://arxiv.org/abs/1908.07873, Aug. 2019

Original

2019

Cited alongside, same era.

N. Yoshida, T. Nishio, M. Morikura, K. Yamamoto, and R. Yonetani, “Hybrid-FL: Cooperative learning mechanism using non-iid data in wireless networks,” [Online] Available: https://arxiv.org/abs/ 1905.07210, May 2019

2019

Cited alongside, same era.

H. H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, “Scheduling policies for federated learning in wireless networks,” [Online] Available: https://arxiv.org/abs/1908.06287, Aug. 2019

Original

2019

Cited alongside, same era.

N. H. Tran, W. Bao, A. Zomaya, Minh N.H. Nguyen, and C. S. Hong, “Federated learning over wireless networks: Optimization model design and analysis,” IEEE Conf. on Computer Commun. (INFOCOM) , Paris, France, May 2019

2019

Closest in time.

M. S. H. Abad, E. Ozfatura, D. Gündüz, and O. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” [Online] Available: https://arxiv.org/abs/1909.02362, Sept. 2019

Original

2019

Closest in time.

M. Mohammadi Amiri, and D. Gündüz, “Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air‌,” IEEE Int. Symp. on Inform. Theory (ISIT) , Paris, France, Jul. 2019

2019

Closest in time.

M. Mohammodi Amiri, and D. Gündüz, “Federated learning over wireless fading channels.” [Online] Available: https://arxiv.org/abs /1907.09769, Jul. 2019

2019

Closest in time.