# Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks
## 1 Introduction
### Related Works  
Intermediate edge servers that are located in base stations between UAVs and a cloud server (usually, more close to the UAVs) can help significantly reduce both communication and computation costs required by the FL [8; 9; 10]. [PERSON] et al. [11] considered a FL system with a ground fusion center (GFC) acting as an aggregator to deploy the UAV network in remote locations in order to reduce the communication complexity. On the other hand, the authors of [12] used the UAVs as a communication link between the users and edge notes to mitigate the network latency overhead. [PERSON] et al. [13] proposed using multi-dimensional contracts to create an incentive mechanism between the UAVs and task publishers, which can increase the number of UAV providers that collaborate with the FL system owners. The authors [14] formulated several services: computational offloading, resource allocation, and optimal UAV location in a mobile edge computing network, while using the UAVs as both a communication and computational device. Almost all of the above works involve UAVs as an active part of the network system, but they do not take data heterogeneity of the generated samples into account. Another important consideration in UAV networks is that the UAVs are practically constrained by the memory, communication, computation, and energy consumption. This leads to a low participation rate of the UAVs, which will eventually affect the performance of the model [15]. Proposed methods that solve the system heterogeneity issues include using distributed learning to perform the training process across multiple edge devices [16; 17], choosing the proficient devices by predicting outages and resource information of the critical infrastructure agents [18]. Even though most of the above-mentioned studies use FL as a base model, they do not consider extreme non-i.i.d. situations that arise when the deployment structures and generated data distributions of the UAV networks among different edge servers differ significantly. Several studies [19; 20; 21] have investigated the performance issue of the FL in non-i.i.d environments, but in the absence of edge servers. Unfortunately, therefore, the results of these works are not applicable to developing an effective FL algorithm in the edge-aided UAV network. The authors of [22; 23] developed several effective FL algorithms for edge-aided networks. However, when the number of edge servers is large, which is the most realistic situation in the edge-aided UAV network, the performance of such algorithms is relatively low (and thus, might be unsatisfactory in practice) due to the wide divergence of data available at the edge servers [24]. In addition, some researchers have tried to include the edge servers in the training process for UAV networks [13; 25; 26; 27; 28], but the edge servers still do not actively participate in the aggregation process.  
To the best of our knowledge, in the literature, the aforementioned limitations have not been broken through yet, and developing high-performing FL algorithms for the edge-aided UAV network that is robust in non-i.i.d. data distributions is still an active area of research.