mlsanityData to 7 Oct 2026

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

arXiv 2301.00912 ↗First posted 3 Jan 2023cs.LG cs.AIcited by 5

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Abstract (arXiv)

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 66 of its 165 references that are in the map. First posted on arXiv 3 Jan 2023.

Builds on

66 of 165 references.

References · 66

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38 more
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50 most cited of 66 All 66 (JSON)

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systemscited by 1
  2. 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learningcited by 1
  3. 2024Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaversecited by 0
  4. 2025Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharingcited by 0
  5. 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centerscited by 0