This paper considers an unmanned aerial vehicle (UAV)-assisted Hierarchical Federated Learning (HFL), where UAVs act as intermediate aggregators. We formulate an optimization problem that aims to find the best UAV placements, user-UAV associations, channel assignments, and user selection to minimize the time needed for HFL to achieve a target learning accuracy. We propose a two-phase approach to solve the problem. The first one deals with the UAV placement using the K-means algorithm. For the second phase, we propose a user association and selection algorithm that prioritizes selecting users who can make a time-efficient and significant contribution to the FL training process. Simulation results show the proposed algorithm’s efficiency and ability to reach the target learning accuracy faster than the considered benchmarks.


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    Title :

    On the Optimization of UAV-Assisted Wireless Networks for Hierarchical Federated Learning


    Contributors:


    Publication date :

    2023-09-05


    Size :

    1172573 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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