关键词: 移动边缘计算, 计算迁移, 机器学习, 联邦学习, 延迟敏感

Abstract: Concerning the challenges of the diversity of data sources, non-independent and identical distribution of data and the heterogeneity of both computing capabilities and energy consumption of edge devices in Internet of Things (IoT), a computation offloading policy in Mobile Edge Computing (MEC) network that deploys both centralized learning and federated learning was proposed. Firstly, a system model of computation offloading related to both centralized learning and federated learning was built, considering the network transmission delay, computation delay and energy consumption of centralized learning and federated learning models. Then, with the system delay minimization as optimization object, considering the constraints of energy consumption and the training times based on machine learning accuracy, a computation offloading optimization model for machine learning was constructed. After that, the game for this computation offloading was formulated and analyzed. Based on the analysis results, an Energy-Constrained Delay-Greedy (ECDG) algorithm was proposed, which found the optimal solutions for the model via a two-stage policy of greedy decision and energy-constrained decision updating. Compared to the centralized-greedy and Federated Learning with Client Selection (FedCS) algorithms, ECDG algorithm has the lowest average learning delay, which is 1/10 of that in the centralized-greedy algorithm, and 1/5 of that in the FedCS algorithm. The experimental results show that, ECDG algorithms can automatically select the optimal machine learning models by computation offloading so that it can efficiently reduce the average machine learning delay, improve the energy efficiency of edge devices and satisfy the Quality of Service (QoS) requirements of IoT applications.

Key words: Mobile Edge Computing (MEC), computation offloading, machine learning, federated learning, delay sensitive