# Energy-Efficient Hierarchical Federated Learning in UAV Networks with Partial AI Model Upload Under Non-Convex Loss

## TL;DR

This paper introduces an energy-efficient method for training AI models in UAV networks by reducing data upload and optimizing energy use.

## Contribution

The novel approach combines partial model upload with Lyapunov optimization to reduce energy consumption in non-convex federated learning.

## Key findings

- The proposed scheme significantly reduces energy consumption compared to traditional HFL.
- It maintains high test accuracy while adapting to mobility and channel constraints.
- Simulation results confirm improved system performance with reduced communication costs.

## Abstract

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance.