Energy-Efficient Federated Learning in Internet of Drones Networks
Jingjing Yao, Xiang Sun
Abstract
Internet of drones (IoD), where drones act as the Internet of things (IoT) devices, makes IoT networks much more flexible and responsive because of high mobility of drones. Machine learning (ML) techniques can be applied in IoD to facilitate multiple applications such as object tracking and traffic surveillance, where ML data samples are collected and analyzed in the edge servers at the ground base station (BS). However, aggregating all data samples incurs huge wireless network traffic and potential data privacy leakage. Federated learning (FL) is then proposed to address these challenges by performing local training in drones and aggregating model parameters at the BS without sharing raw data samples. The FL performance in IoD networks is greatly affected by limited drone batteries which power FL local training, wireless data transmission, and drones’ movements. This paper hence investigates the energy-efficient FL in IoD networks to optimize CPU frequencies of drones’ on-board computing units such that total energy consumption of all the drones in the FL process can be minimized, while satisfying the FL training time requirement. We formulate the problem as a non-linear programming problem and then design an algorithm with polynomial time complexity to derive the optimum solution. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.