Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

Jamshid Tursunboev,Dong-Woo Lim,Sung-Bum Huh,Heechul Jung,Jae-Mo Kang,Yong-Sung Kang

doi:10.3390/app12020670

Abstract

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.

Highlights

Owing to the recent advances in the Internet of Things (IoT), there has been a bursting increase in heterogeneous and private data generated from various sensors, mobile phones, smart home appliances and unmanned aerial vehicles (UAVs) [1]
To consider the situation with extremely non-i.i.d. data distribution, 100 UAVs and 10 edge servers are selected such that each UAV is given the data samples only with one class and each edge server is assigned 10 UAVs with 2 different classes in total
The purpose of considering this scenario is to study the impact of feature distribution skew on the Federated learning (FL), where Pi is set to be different among the UAVs

Summary

Introduction

Owing to the recent advances in the Internet of Things (IoT), there has been a bursting increase in heterogeneous and private data generated from various sensors, mobile phones, smart home appliances and unmanned aerial vehicles (UAVs) [1]. Federated learning (FL) is an efficient and promising solution to realize such a goal [5], and its applications in unmanned aerial vehicle (UAV) networks have attracted significant attention in both industrial and academic sectors [4]. With the aid of FL, a global machine learning model can be efficiently trained without the need for each UAV to directly send its private data to a cloud (or edge) server. This can be realized through the following four cyclic steps: (i) training a global model locally at each UAV (i.e., local model) with its own data, (ii) reporting the trained local models of the UAVs to a centralized cloud server,

Methods

Results

Conclusion