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This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterogeneous computing and communication capacities.
1903
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1909
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1911
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Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proceedings of IEEE , vol. 86, no. 11, 1998
1998
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A. D. Pia, S. S. Dey, and M. Molinaro, “Mixed-integer Quadratic Programming is in NP,” pp. 1–10, 2014
2014
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Z. Wei, S. Gupta, X. Lian, and J. Liu, “Staleness-Aware Async-SGD for distributed deep learning,” IJCAI International Joint Conference on Artificial Intelligence , vol. 2016-Janua, pp. 2350–2356, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
U. Y. Mohammad and S. Sorour, “Multi-Objective Resource Optimization for Hierarchical Mobile Edge Computing,” in 2018 IEEE Global Communications Conference: Mobile and Wireless Networks (Globecom2018 MWN) , Abu Dhabi, United Arab Emirates, dec 2018
2018
——, “Adaptive Federated Learning in Resource Constrained Edge Computing Systems,” IEEE Journal on Selected Areas in Communications , no. Early Access, pp. 1–1, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8664630/
2019
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U. Mohammad and S. Sorour, “Adaptive Task Allocation for Mobile Edge Learning,” in 2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW) . IEEE, apr 2019, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/8902527/
2019
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K. Gyarmathy, “Comprehensive Guide to IoT Statistics You Need to Know in 2020,” 2020. [Online]. Available: https://www.vxchnge.com/blog/iot-statistics
2020
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Rhea Kelly, “Internet of Things Data To Top 1.6 Zettabytes by 2020 – Campus Technology,” 2015. [Online]. Available: https://campustechnology.com/articles/2015/04/15/internet-of-things-data-to-top-1-6-zettabytes-by-2020.aspx
2020
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Cited alongside, same era.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When Edge Meets Learning : Adaptive Control for Resource-Constrained Distributed Machine Learning,” in INFOCOM , 2018. [Online]. Available: https://researcher.watson.ibm.com/researcher/files/us-wangshiq/SW{_}INFOCOM2018.pdf
2018
Cited alongside, same era.
U. Y. Mohammad, S. Sorour, and M. S. Hefeida, “Task allocation for mobile federated and offloaded learning with energy and delay constraints,” in IEEE ICC 2020 Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond (IEEE ICC’20 Workshop - EML5G) , Dublin, Ireland, Jun. 2020
2020
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