Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

• URL: http://arxiv.org/abs/2010.15371v2
• Date: Tue, 22 Dec 2020 08:28:24 GMT
• Title: Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
• Authors: Liangkai Zhou, Yuncong Hong, Shuai Wang, Ruihua Han, Dachuan Li, Rui Wang, and Qi Hao
• Abstract summary: Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications. Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. This paper proposes the learning centric wireless resource allocation scheme that maximizes the worst learning performance of multiple tasks.
• Score: 15.577056429740951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the generalization error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.

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