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Federated learning (FL) has recently become one of the hottest focuses inwireless edge networks with the ever-increasing computing capability of userequipment (UE). In FL, UEs train local machine learning models and transmitthem to an aggregator, where a global model is formed and then sent back toUEs. In wireless networks, local training and model transmission can beunsuccessful due to constrained computing resources, wireless channelimpairments, bandwidth limitations, etc., which degrades FL performance inmodel accuracy and/or training time. Moreover, we need to quantify the benefitsand cost of deploying edge intelligence, as model training and transmissionconsume certain amount of resources. Therefore, it is imperative to deeplyunderstand the relationship between FL performance and multiple-dimensionalresources. In this paper, we construct an analytical model to investigate therelationship between the FL model accuracy and consumed resources in FLempowered wireless edge networks. Based on the analytical model, we explicitlyquantify the model accuracy, available computing resources and communicationresources. Numerical results validate the effectiveness of our theoreticalmodeling and analysis, and demonstrate the trade-off between the communicationand computing resources for achieving a certain model accuracy.

Resource Consumption for Supporting Federated Learning in Wireless Networks