# Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory

arXiv: https://arxiv.org/abs/2003.04451 · HTML: https://mlsanity.com/paper/2003.04451/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning](https://mlsanity.com/paper/2205.11779/) (2022)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance](https://mlsanity.com/paper/2501.08655/) (2025)
- [FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks](https://mlsanity.com/paper/2404.14811/) (2024)
- [Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding](https://mlsanity.com/paper/2112.03267/) (2021)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things](https://mlsanity.com/paper/2407.00814/) (2024)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [Degree of Staleness-Aware Data Updating in Federated Learning](https://mlsanity.com/paper/2508.16931/) (2025)
- [MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria](https://mlsanity.com/paper/2605.05492/) (2026)

## Abstract (arXiv)

This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean-field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations.