# Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning

arXiv: https://arxiv.org/abs/2308.07273 · HTML: https://mlsanity.com/paper/2308.07273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Do We Train on Test Data? Purging CIFAR of Near-Duplicates](https://mlsanity.com/paper/1902.00423/) (2019)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)
- [Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities](https://mlsanity.com/paper/2104.07557/) (2021)
- [3D Aerial Highway: The Key Enabler of the Retail Industry Transformation](https://mlsanity.com/paper/2009.09477/) (2020)
- [Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks](https://mlsanity.com/paper/2101.12472/) (2021)
- [Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones](https://mlsanity.com/paper/2201.01230/) (2022)
- [Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs](https://mlsanity.com/paper/2106.14276/) (2021)
- [Dynamics of Laser-Charged UAVs: A Battery Perspective](https://mlsanity.com/paper/2008.13316/) (2020)
- [On the Downlink Performance of RSMA-based UAV Communications](https://mlsanity.com/paper/2011.05019/) (2020)
- [Multi-UAV Data Collection Framework for Wireless Sensor Networks](https://mlsanity.com/paper/1910.10792/) (2019)
- [Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting](https://mlsanity.com/paper/2005.13122/) (2020)
- [Optimization of Wireless Relaying With Flexible UAV-Borne Reflecting Surfaces](https://mlsanity.com/paper/2006.10969/) (2020)
- [On the Optimal 3D Placement of a UAV Base Station for Maximal Coverage of UAV Users](https://mlsanity.com/paper/2008.09262/) (2020)
- [UAV-based Crowd Surveillance in Post COVID-19 Era](https://mlsanity.com/paper/2111.14176/) (2021)

## Built on by (most cited first, 2 of 2)

- [Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity](https://mlsanity.com/paper/2504.16515/) (2025)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model's accuracy, under UAV constraints of energy consumption, communication quality, and local datasets' heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.