University of Oulu

T. Zeng, O. Semiari, M. Mozaffari, M. Chen, W. Saad and M. Bennis, "Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148776

Federated learning in the sky : joint power allocation and scheduling with UAV swarms

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Author: Zeng, Tengchan1; Semiari, Omid2; Mozaffari, Mohammad3;
Organizations: 1Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061 USA
2Department of Electrical and Computer Engineering, University of Colorado Colorado Springs, Colorado Springs, CO, 80918 USA
3Ericsson Research, Santa Clara, CA, 95054 USA
4Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544 USA
5Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-18


Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm’s control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-5089-5
ISBN Print: 978-1-7281-5090-1
Article number: 9148776
DOI: 10.1109/ICC40277.2020.9148776
Host publication: 2020 IEEE International Conference on Communications, ICC 2020
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported, in part, by the U.S. National Science Foundation under Grants CNS-1739642 and CNS-1941348, and by the Academy of Finland Project CARMA, by the Academy of Finland Project MISSION, by the Academy of Finland Project SMARTER, as well as by the INFOTECH Project NOOR.
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