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Tradeoff between Model Accuracy and Cost for Federated Learning in the Mobile Edge Computing Systems

机译：移动边缘计算系统联合学习的模型准确性与成本之间的权衡

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摘要

The combination of MEC and federated learning is a promising research direction in network intelligence. However, the performance and efficiency of federated learning cannot be guaranteed in MEC systems. In this paper, in order to balance the model accuracy and resource consumption of federated learning in MEC systems, a framework for deploying federated learning in MEC systems is proposed. Based on this framework, the model accuracy performance of federated learning is analyzed, and a tractable upper bound of accuracy loss is given. In addition, an optimization problem is proposed to balance the accuracy loss and resource consumption of the training model, and a joint optimization algorithm with high computational efficiency is designed to approach the optimal solution. Finally, numerical simulation and experimental results show that the joint optimization algorithm can not only improve the model accuracy of federated learning, but also significantly reduce the resource consumption.
机译：MEC和联合学习的结合是网络智能的有希望的研究方向。 但是，在MEC系统中无法保证联合学习的性能和效率。 在本文中，为了平衡MEC系统中联合学习的模型准确性和资源消耗，提出了一种在MEC系统中部署联合学习的框架。 基于该框架，分析了联合学习的模型精度性能，并给出了精度损耗的易缩小界限。 此外，提出了优化问题来平衡训练模型的精度损耗和资源消耗，并且设计具有高计算效率的联合优化算法旨在接近最佳解决方案。 最后，数值模拟和实验结果表明，联合优化算法不仅可以提高联邦学习的模型准确性，还可以显着降低资源消耗。

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