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

arXiv: https://arxiv.org/abs/2104.07557 · HTML: https://mlsanity.com/paper/2104.07557/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Accelerating DNN Training in Wireless Federated Edge Learning Systems](https://mlsanity.com/paper/1905.09712/) (2019)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Federated Learning for Ultra-Reliable Low-Latency V2V Communications](https://mlsanity.com/paper/1805.09253/) (2018)
- [Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection](https://mlsanity.com/paper/2002.09692/) (2020)

## Built on by (most cited first, 22 of 22)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout](https://mlsanity.com/paper/2210.16105/) (2022)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Compression with Exact Error Distribution for Federated Learning](https://mlsanity.com/paper/2310.20682/) (2023)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning](https://mlsanity.com/paper/2308.07273/) (2023)
- [Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size](https://mlsanity.com/paper/2309.03469/) (2023)
- [Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning](https://mlsanity.com/paper/2407.04460/) (2024)
- [Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks](https://mlsanity.com/paper/2402.05973/) (2024)
- [Energy-Aware Decentralized Learning with Intermittent Model Training](https://mlsanity.com/paper/2407.01283/) (2024)
- [The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions](https://mlsanity.com/paper/2501.11714/) (2025)
- [Decentralized Federated Learning With Energy Harvesting Devices](https://mlsanity.com/paper/2602.14051/) (2026)
- [Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks](https://mlsanity.com/paper/2304.06551/) (2023)
- [The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies](https://mlsanity.com/paper/2310.07471/) (2023)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains](https://mlsanity.com/paper/2412.02863/) (2024)
- [Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach](https://mlsanity.com/paper/2512.24694/) (2025)

## 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.