Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT

• URL: http://arxiv.org/abs/2503.06145v3
• Date: Mon, 13 Oct 2025 02:50:18 GMT
• Title: Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT
• Authors: Xiaohong Yang, Minghui Liwang, Liqun Fu, Yuhan Su, Seyyedali Hosseinalipour, Xianbin Wang, Yiguang Hong,
• Abstract summary: This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions.<n>We propose a novel approach to minimize global training costs by integrating learning configuration, bandwidth allocation, and device-to-UAV association.<n> Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.
• Score: 25.924115037501952
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermediate aggregation layers, enabling distributed learning in geographically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV connectivity and is NP-hard. To tackle this, we decompose it into three subproblems: \textit{(i)} optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; \textit{(ii)} introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to-UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; \textit{(iii)} developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

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