FedBA: Non-IID Federated Learning Framework in UAV Networks

TL;DR

This paper introduces FedBA, a federated learning framework tailored for UAV networks that addresses data heterogeneity and privacy concerns, demonstrating improved accuracy over existing methods through real dataset experiments.

Contribution

The paper proposes FedBA, a novel federated learning algorithm designed specifically for UAV networks to handle data heterogeneity and enhance model accuracy.

Findings

FedBA outperforms existing algorithms in accuracy.

It effectively addresses data heterogeneity in UAV networks.

Experimental results validate the algorithm's effectiveness.

Abstract

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.