Multi-server federated edge learning for low power consumption wireless resource allocation based on user QoE

Abstract

Federated edge learning (FEL) deploys a machine learning algorithm by using devices distributed on the edge of a network, trains massive local data, uploads the local model to update the parameters after training, and performs alternate updating with global model parameters to reduce the pressure for uplink data transmission, prevent systematic time delay and ensure data security. This paper proposes that an optimal balance between time delay and energy consumption be achieved by optimizing the transmission power and bandwidth allocation based on user quality of experience (QoE) in a multi-server intelligent edge network. Given the limited computing capability of devices involved in FEL local training, the transmission power is modeled as a quasi-convex uplink power allocation (UPA) problem, and a lower energy consumption bandwidth allocation algorithm is proposed for solution-seeking. The proposed algorithm allocates appropriate power to the device by adapting the computing power and channel state of the device, thereby reducing energy consumption. As the theoretical deduction result suggests that additional bandwidth should be allocated to those devices with weak computing capabilities and poor channel conditions to realize minimal energy consumption within the restraint time. The simulation result indicates that, the maximum gain of the proposed algorithm can be optimized by 31% compared with the baseline.