mlsanityData to 7 Oct 2026

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

arXiv 2503.06145 ↗First posted 8 Mar 2025cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 58 references that are in the map. First posted on arXiv 8 Mar 2025.

Builds on

29 of 58 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  5. 2016AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classificationcited by 343
  6. 2021Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemblecited by 211
  7. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  8. 2023Policy Expansion for Bridging Offline-to-Online Reinforcement Learningcited by 103
  9. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  10. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 74
  11. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  12. 2020Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Managementcited by 26
17 more
  1. 2017Iteration complexity of inexact augmented Lagrangian methods for constrained convex programmingcited by 21
  2. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  3. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  4. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  5. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  6. 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10
  7. 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
  8. 2022Energy-Aware Edge Association for Cluster-based Personalized Federated Learningcited by 6
  9. 2023UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformercited by 6
  10. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  11. 2022Time Minimization in Hierarchical Federated Learningcited by 4
  12. 2022A Survey on UAV-enabled Edge Computing: Resource Management Perspectivecited by 4
  13. 2023Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfercited by 4
  14. 2024Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Controlcited by 4
  15. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  16. 20253D-TrIM: A Memory-Efficient Spatial Computing Architecture for Convolution Workloadscited by 1
  17. 2025Cellular Automaton With CNNcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edgecited by 0
  2. 2026When Agentic AI Meets Integrated Sensing and Communicationcited by 0