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Article · IEEE Transactions on Machine Learning in Communications and Networking · January 1, 2025

UAV-Assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis

Ruslan Zhagypar, Nour Kouzayha, Hesham ElSawy, Hayssam Dahrouj, Tareq Y. Al-Naffouri

Ruslan Zhagypar
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Abstract

The development of the sixth-generation (6G) of wireless networks is driving computation toward the network edge, where Hierarchical Federated Learning (HFL) plays a pivotal role in distributing learning across edge devices. In HFL, edge devices train local models and send updates to an edge server for local aggregation, which are then forwarded to a central server for global aggregation. However, the unreliability of communication channels at the edge and backhaul links poses a significant bottleneck for HFL-enabled systems. To address this challenge, this paper proposes an unbiased HFL algorithm for Uncrewed Aerial Vehicle (UAV)-assisted wireless networks. While applicable to terrestrial base stations (BSs), the proposed algorithm relies on UAVs for local model aggregation thanks to their ability to enhance wireless channels with lower latency and improved coverage. The proposed algorithm adjusts update weights during local and global aggregations at UAVs to mitigate the impact of unreliable channels. To quantify channel unreliability in HFL, stochastic geometry tools are employed to assess success probabilities of local and global model parameter transmissions. Incorporating these metrics aims to mitigate biases towards devices with better channel conditions in UAV-assisted networks. The paper further examines the theoretical convergence of the proposed unbiased UAV-assisted HFL algorithm under adverse channel conditions and highlights the impact of the limited battery capacity of the UAV on the efficiency of the HFL algorithm. Additionally, the algorithm facilitates optimization of system parameters such as UAV count, altitude, battery capacity, etc. The simulation results underscore the effectiveness of the proposed unbiased HFL scheme, demonstrating a 5.5% higher accuracy and approximately 85% faster convergence compared to conventional HFL algorithms. We make our code available at the following GitHub repository:$\texttt {UAV-assisted Unbiased HFL Code}$.

Privacy-Preserving Technologies in DataUAV Applications and OptimizationIoT and Edge/Fog Computinghierarchical federated learningUAV-assisted networksedge computing

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