mlsanityData to 14 Oct 2026

Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing

arXiv 2503.09144 ↗First posted 12 Mar 2025cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

UAV swarms are widely used in emergency communications, area monitoring, and disaster relief. Coordinated by control centers, they are ideal for federated learning (FL) frameworks. However, current UAV-assisted FL methods primarily focus on single tasks, overlooking the need for multi-task training. In disaster relief scenarios, UAVs perform tasks such as crowd detection, road feasibility analysis, and disaster assessment, which exhibit time-varying demands and potential correlations. In order to meet the time-varying requirements of tasks and complete multiple tasks efficiently under resource constraints, in this paper, we propose a UAV swarm based multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks efficiently under constrained energy and bandwidth resources. Through theoretical analysis, we identify key factors affecting task performance and introduce a task attention mechanism to dynamically evaluate task importance, thereby achieving efficient resource allocation. Additionally, we propose a task affinity (TA) metric to capture the dynamic correlation among tasks, thereby promoting task knowledge sharing to accelerate training and improve the generalization ability of the model in different scenarios. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply a block coordinate descent method for optimization. For the outer problem, a two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption and multi-task performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 64 references that are in the map. First posted on arXiv 12 Mar 2025.

Builds on

30 of 64 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  4. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  5. 2017Federated Multi-Task Learningcited by 672
  6. 2019Federated Learning with Personalization Layerscited by 541
  7. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  9. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  10. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  11. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  12. 2021Efficiently Identifying Task Groupings for Multi-Task Learningcited by 222
18 more
  1. 2018Federated Learning via Over-the-Air Computationcited by 219
  2. 2019AdaShare: Learning What To Share For Efficient Deep Multi-Task Learningcited by 160
  3. 2020Understanding and Improving Information Transfer in Multi-Task Learningcited by 114
  4. 2022Federated Learning with Partial Model Personalizationcited by 114
  5. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  6. 2021RotoGrad: Gradient Homogenization in Multitask Learningcited by 83
  7. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  8. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  9. 2020Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Managementcited by 26
  10. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  11. 2022Federated Learning via Unmanned Aerial Vehiclecited by 11
  12. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  13. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  14. 2024FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharingcited by 8
  15. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  16. 2024UAV-Enabled Asynchronous Federated Learningcited by 3
  17. 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learningcited by 1
  18. 2025UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharingcited by 1

Built on by

Not cited yet.