Enhancing Federated Learning in Fog-Aided IoT by CPU Frequency and Wireless Power Control

Machine learning models have been built in fog nodes in fog-aided Internet-of-Things (IoT) networks to provision future events prediction and image classification by training data collected from IoT devices. However, sending massive data from all devices to a fog node incurs huge network traffic in wireless links in between. Federated learning is proposed to address the challenge by training models locally in IoT devices and only sharing model parameters in the fog node. In this article, we investigate both the CPU frequency control and wireless transmission power control of all IoT devices to balance the tradeoff between the device energy consumption and federated learning time (consisting of both the computation and communication latencies) in fog-aided IoT networks. We formulate the joint optimization of CPU and power control as a nonlinear programming (NLP) problem with the objective to minimize the energy consumption of all IoT devices constrained by the federated learning time requirement. An alternative direction algorithm, which alternatively optimizes the CPU frequency and wireless transmission power until convergence, is hence designed to solve this problem and its performance is demonstrated via extensive simulations.

Automated summary

In this article, we investigate the optimization of energy consumption and federated learning time in fog-aided IoT networks by controlling the CPU frequency and wireless transmission power of all IoT devices. An alternative direction algorithm is designed to solve this problem by optimizing the CPU frequency and wireless transmission power alternately until convergence, and its performance is demonstrated through extensive simulations.