Joint Coverage and Resource Allocation for Federated Learning in UAV-Enabled Networks

Thanks to its communication efficiency and low latency, federated learning (FL) has emerged as a promising learning paradigm in the unmanned aerial vehicle (UAV)-enabled networks; nevertheless, the great potential of FL in UAV networks is realizable only upon optimizing crucial factors such as coverage and transmission delay. In this paper, we study the problem of joint coverage optimization and efficient radio resource allocation. The objective is to minimize the convergence time of FL in a UAV-enabled network, where UAVs perform learning over an inhomogeneous sensor network. To this end, we develop a method that minimizes the FL computation and communication time in each global iteration: First, the algorithm adjusts the UAVs’ locations to control the average number of sensors associated with each UAV to maximize the coverage and to reduce the overall computation time. The UAVs’ locations also affect their transmission delay. Thus, in the second step, the method uses a fair resource allocation scheme for channel allocation and power control to minimize the FL communication time while retaining the efficiency of resource expenditure.

I. Introduction

Recently, unmanned aerial vehicle (UAV)-enabled networking has emerged as a new paradigm for managing the complexity of processing the excessive amount of data. To this end, UAVs cooperate in learning by taking advantage of their implemented machine learning algorithms [1]. While centralized machine learning algorithms might be impractical due to the UAVs’ mobility and the scarcity of radio resources, distributed learning is envisioned as a potential alternative.