# Characterization of the Global Bias Problem in Aerial Federated Learning

arXiv: https://arxiv.org/abs/2212.14360 · HTML: https://mlsanity.com/paper/2212.14360/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [Federated Learning via Unmanned Aerial Vehicle](https://mlsanity.com/paper/2210.10970/) (2022)
- [On the Design of Communication-Efficient Federated Learning for Health Monitoring](https://mlsanity.com/paper/2211.16952/) (2022)
- [Analysis of Large Scale Aerial Terrestrial Networks with mmWave Backhauling](https://mlsanity.com/paper/2106.07000/) (2021)
- [UAV-Aided Multi-Community Federated Learning](https://mlsanity.com/paper/2206.02043/) (2022)

## Built on by (most cited first, 3 of 3)

- [UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis](https://mlsanity.com/paper/2407.07739/) (2024)
- [Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching](https://mlsanity.com/paper/2501.11745/) (2025)
- [Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients](https://mlsanity.com/paper/2401.09267/) (2024)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of aerial FL in large-scale UAV networks. To this end, the paper proposes a channel-aware distribution and aggregation scheme to enforce equal contribution from all devices in the FL training as a means to resolve the global bias problem. We demonstrate the convergence of the proposed method by experimenting with the MNIST dataset and show its superiority compared to existing methods. The obtained results enable system parameter tuning to relieve the impact of the aerial channel deficiency on the FL convergence rate.