mlsanityData to 7 Oct 2026

Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities

arXiv 2104.07557 ↗First posted 15 Apr 2021cs.NI cs.LGcited by 22

Not on the map. Why?

Abstract (arXiv)

Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, textit(i.e.), federated learning (FL), has been recently proposed to enable multiple UAVs to collaboratively train ML model without letting out raw data. However, almost all existing FL paradigms are still centralized, textiti.e., a central entity is in charge of ML model aggregation and fusion over the whole network, which could result in the issue of a single point of failure and are inappropriate to UAV networks with both unreliable nodes and links. Thus motivated, in this article, we propose a novel architecture called DFL-UN (underlineDecentralized underlineFederated underlineLearning for underlineUAV underlineNetworks), which enables FL within UAV networks without a central entity. We also conduct a preliminary simulation study to validate the feasibility and effectiveness of the DFL-UN architecture. Finally, we discuss the main challenges and potential research directions in the DFL-UN.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

8 of 24 references.

References · 8

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  5. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  6. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  7. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  8. 2020Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selectioncited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 7; 2025: 3; 2026: 1.

22 papers.

Sort:

  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2023A Survey on Decentralized Federated Learningcited by 27
  4. 2022Federated Learning in Satellite Constellationscited by 21
  5. 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
  6. 2022Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropoutcited by 12
  7. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  8. 2023Compression with Exact Error Distribution for Federated Learningcited by 9
  9. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  10. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  11. 2023Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learningcited by 2
  12. 2023Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Sizecited by 2
10 more
  1. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  2. 2024Energy-Aware Decentralized Learning with Intermittent Model Trainingcited by 1
  3. 2025The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directionscited by 1
  4. 2026Decentralized Federated Learning With Energy Harvesting Devicescited by 1
  5. 2023Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networkscited by 0
  6. 2023The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistenciescited by 0
  7. 2024Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networkscited by 0
  8. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  9. 2024Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domainscited by 0
  10. 2025Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approachcited by 0