How Many Resources Are Needed to Support Wireless Edge Networks
Yi‐Jing Liu, Gang Feng, Yao Sun, Shuang Qin · 2023
Federated learning (FL) has recently become one of the most potential techniques in wireless edge networks with the ever-increasing computing capability of user equipment (UE), as it can facilitate collaborative training of individual models while enhancing data security. In conventional FL frameworks, UEs usually train local machine learning (ML) models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless edge networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, and bandwidth limitations, which significantly degrades FL performance such as model accuracy and training time. Moreover, we need to quantify the benefits and costs of deploying FL in wireless edge networks, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this chapter, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL-enabled wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, computing resources, and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy.