Federated Learning in UAV-Enhanced Networks: Joint Coverage and
  Convergence Time Optimization
Mariam Yahya Setareh Maghsudi Slawomir Stanczak
Federated learning (FL) involves several devices that collaboratively train a
shared model without transferring their local data. FL reduces the
communication overhead, making it a promising learning method in UAV-enhanced
wireless networks with scarce energy resources. Despite the potential,
implementing FL in UAV-enhanced networks is challenging, as conventional UAV
placement methods that maximize coverage increase the FL delay significantly.
Moreover, the uncertainty and lack of a priori information about crucial
variables, such as channel quality, exacerbate the problem. In this paper, we
first analyze the statistical characteristics of a UAV-enhanced wireless sensor
network (WSN) with energy harvesting. We then develop a model and solution
based on the multi-objective multi-armed bandit theory to maximize the network
coverage while minimizing the FL delay. Besides, we propose another solution
that is particularly useful with large action sets and strict energy
constraints at the UAVs. Our proposal uses a scalarized best-arm identification
algorithm to find the optimal arms that maximize the ratio of the expected
reward to the expected energy cost by sequentially eliminating one or more arms
in each round. Then, we derive the upper bound on the error probability of our
multi-objective and cost-aware algorithm. Numerical results show the
effectiveness of our approach.