Delay Optimization of a Federated Learning-based UAV-aided IoT network

TL;DR

This paper proposes a federated learning framework integrated with SWIPT in UAV-assisted IoT networks, enabling energy-efficient data aggregation and communication without draining device batteries.

Contribution

It introduces a novel UAV-based FL architecture utilizing SWIPT for energy harvesting and data transmission in IoT networks.

Findings

FL is feasible in UAV-assisted IoT with SWIPT

Energy constraints are effectively managed

Communication efficiency is maintained

Abstract

This paper explores the integration of power splitting(PS) simultaneous wireless information and power transfer (SWIPT) architecture and federated learning (FL) in Internet of Things (IoT) networks. The use of SWIPT allows power-constrained devices to simultaneously harvest energy and transmit data, addressing the energy limitations faced by IoT devices. The proposed scenario involves an Unmanned Arial Vehicle (UAV) serving as the base station (BS) and edge server, aggregating weight updates from IoT devices and unicasting aggregated updates to each device. The results demonstrate the feasibility of FL in IoT scenarios, ensuring communication efficiency without depleting device batteries.