University of Oulu

H. Shiri, J. Park and M. Bennis, "Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory," in IEEE Transactions on Communications, vol. 68, no. 11, pp. 6840-6857, Nov. 2020, doi: 10.1109/TCOMM.2020.3017281

Communication-efficient massive UAV online path control: federated learning meets mean-field game theory

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Author: Shiri, Hamid1; Park, Jihong2; Bennis, Mehdi1
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
2School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-12-03


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.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 68
Issue: 11
Pages: 6840 - 6857
DOI: 10.1109/TCOMM.2020.3017281
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by Academy of Finland under Grant 294128, in part by the 6Genesis Flagship under Grant 318927, in part by the Kvantum Institute Strategic Project NOOR, in part by the EU-CHISTERA projects LeadingEdge and CONNECT, and in part by the Academy of Finland through the MISSION Project under Grant 319759.
Academy of Finland Grant Number: 294128
Detailed Information: 294128 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319759 (Academy of Finland Funding decision)
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