Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998

1998

Earlier work this paper cites.

F. Topsok, “Some bounds for the logarithmic function,” Inequality theory and applications , vol. 4, 2006

2006

Earlier work this paper cites.

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009

2009

Earlier work this paper cites.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010 . Springer, 2010, pp. 177–186

2010

Earlier work this paper cites.

A. Agarwal and J. C. Duchi, “Distributed delayed stochastic optimization,” in Advances in Neural Information Processing Systems , 2011, pp. 873–881

2011

Earlier work this paper cites.

Y. Zhang, M. J. Wainwright, and J. C. Duchi, “Communication-efficient algorithms for statistical optimization,” in Advances in Neural Information Processing Systems , 2012, pp. 1502–1510

2012

Earlier work this paper cites.

H. Kahraman, S.Sagiroglu, and I.Colak, “Developing intuitive knowledge classifier and modeling of users’ domain dependent data in web,” Knowledge Based Systems , vol. 37, pp. 283–295, 2013

2013

Earlier work this paper cites.

S. Shalev-Shwartz and S. Ben-David, Understanding machine learning: From theory to algorithms . Cambridge university press, 2014

2014

Earlier work this paper cites.

M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su, “Scaling distributed machine learning with the parameter server.” in OSDI , vol. 14, 2014, pp. 583–598

2014

Earlier work this paper cites.

X. Lian, Y. Huang, Y. Li, and J. Liu, “Asynchronous parallel stochastic gradient for nonconvex optimization,” in Advances in Neural Information Processing Systems , 2015, pp. 2737–2745

2015

Earlier work this paper cites.

Y. Arjevani and O. Shamir, “Communication complexity of distributed convex learning and optimization,” in Advances in neural information processing systems , 2015, pp. 1756–1764

2015

Earlier work this paper cites.

S. Bubeck, “Convex optimization: Algorithms and complexity,” Foundations and trends in Machine Learning , vol. 8, no. 3-4, 2015

2015

Earlier work this paper cites.

M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,” IEEE Internet of Things Journal , vol. 3, no. 6, pp. 854–864, Dec. 2016

2016

Earlier work this paper cites.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org

2016

Earlier work this paper cites.

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2016

2016

Earlier work this paper cites.

L. Tong and W. Gao, “Application-aware traffic scheduling for workload offloading in mobile clouds,” in IEEE INFOCOM , Apr. 2016

2016

Cited alongside, same era.

L. Tong, Y. Li, and W. Gao, “A hierarchical edge cloud architecture for mobile computing,” in IEEE INFOCOM , Apr. 2016

2016

Cited alongside, same era.

J. Chen, R. Monga, S. Bengio, and R. Jozefowicz, “Revisiting distributed synchronous SGD,” in ICLR Workshop Track , 2016

2016

Cited alongside, same era.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for federated learning on user-held data,” in NIPS Workshop on Private Multi-Party Machine Learning , 2016

2016

Cited alongside, same era.

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

S. Zheng, Q. Meng, T. Wang, W. Chen, N. Yu, Z.-M. Ma, and T.-Y. Liu, “Asynchronous stochastic gradient descent with delay compensation,” in Proceedings of the 34th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. International Convention Centre, Sydney, Australia: PMLR, Aug. 2017, pp. 4120–4129

2017

Later among the works it cites.

X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu, “Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,” in Advances in Neural Information Processing Systems , 2017, pp. 5330–5340

2017

Later among the works it cites.

X. Lian, W. Zhang, C. Zhang, and J. Liu, “Asynchronous decentralized parallel stochastic gradient descent,” arXiv preprint arXiv:1710.06952 , 2017

Original

2017

Later among the works it cites.