论文标题
学习中心的无线资源分配用于边缘计算：算法和实验
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
论文作者
论文摘要
Edge Intelligence是一种新兴的网络体系结构，它将传感，通信，计算组件集成并支持各种机器学习应用程序，其中一个基本的交流问题是：如何分配有限的无线资源（例如时间，能源），以同时对异构学习任务的模型培训？现有方法忽略了两个重要事实：1）不同模型对培训数据有异质要求； 2）模拟环境与现实环境之间存在不匹配。结果，它们在实践中可能会导致学习表现较低。本文提出了以学习为中心的无线资源分配（LCWRA）方案，以最大程度地提高多个任务的学习绩效。分析表明，最佳传输时间与概括误差有关。最后，提供了模拟和实验结果，以验证提出的LCWRA方案的性能及其在实际实施中的鲁棒性。
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.
