# UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis arXiv: https://arxiv.org/abs/2407.07739 ยท HTML: https://mlsanity.com/paper/2407.07739/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 64 references that are in the map. - First posted on arXiv 5 Jul 2024. ## Builds on (most cited first, 25 of 25) - [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019) - [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021) - [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019) - [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018) - [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020) - [Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks](https://mlsanity.com/paper/2007.09511/) (2020) - [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020) - [Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD](https://mlsanity.com/paper/2010.12998/) (2020) - [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020) - [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021) - [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022) - [Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles](https://mlsanity.com/paper/2007.06378/) (2020) - [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021) - [Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks](https://mlsanity.com/paper/2303.10580/) (2023) - [UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning](https://mlsanity.com/paper/2209.11624/) (2022) - [On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey](https://mlsanity.com/paper/2402.00881/) (2024) - [Self-Evolving Integrated Vertical Heterogeneous Networks](https://mlsanity.com/paper/2106.13950/) (2021) - [E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI](https://mlsanity.com/paper/2008.01553/) (2020) - [Characterization of the Global Bias Problem in Aerial Federated Learning](https://mlsanity.com/paper/2212.14360/) (2022) - [Energy-efficient Resource Allocation for Mobile Edge Computing Aided by Multiple Relays](https://mlsanity.com/paper/2004.03821/) (2020) - [Analysis of Large Scale Aerial Terrestrial Networks with mmWave Backhauling](https://mlsanity.com/paper/2106.07000/) (2021) - [Energy-aware Trajectory Optimization for UAV-mounted RIS and Full-duplex Relay](https://mlsanity.com/paper/2401.12107/) (2024) - [Fundamentals of Modeling Finite Wireless Networks using Binomial Point Process](https://mlsanity.com/paper/1606.04405/) (2016) - [Downlink Coverage Analysis for a Finite 3D Wireless Network of Unmanned Aerial Vehicles](https://mlsanity.com/paper/1701.01212/) (2017) - [On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks](https://mlsanity.com/paper/2104.01461/) (2021) ## Built on by (most cited first, 2 of 2) - [Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT](https://mlsanity.com/paper/2503.06145/) (2025) - [Personalized Federated Learning by Energy-Efficient UAV Communications](https://mlsanity.com/paper/2605.25212/) (2026) ## Abstract (arXiv) The development of the sixth generation (6G) of wireless networks is bound to streamline the transition of computation and learning towards the edge of the network. Hierarchical federated learning (HFL) becomes, therefore, a key paradigm to distribute learning across edge devices to reach global intelligence. In HFL, each edge device trains a local model using its respective data and transmits the updated model parameters to an edge server for local aggregation. The edge server, then, transmits the locally aggregated parameters to a central server for global model aggregation. The unreliability of communication channels at the edge and backhaul links, however, remains a bottleneck in assessing the true benefit of HFL-empowered systems. To this end, this paper proposes an unbiased HFL algorithm for unmanned aerial vehicle (UAV)-assisted wireless networks that counteracts the impact of unreliable channels by adjusting the update weights during local and global aggregations at UAVs and terrestrial base stations (BS), respectively. To best characterize the unreliability of the channels involved in HFL, we adopt tools from stochastic geometry to determine the success probabilities of the local and global model parameter transmissions. Accounting for such metrics in the proposed HFL algorithm aims at removing the bias towards devices with better channel conditions in the context of the considered UAV-assisted network.. The paper further examines the theoretical convergence guarantee of the proposed unbiased UAV-assisted HFL algorithm under adverse channel conditions. One of the developed approach's additional benefits is that it allows for optimizing and designing the system parameters, e.g., the number of UAVs and their corresponding heights. The paper results particularly highlight the effectiveness of the proposed unbiased HFL scheme as compared to conventional FL and HFL algorithms.