  Federated edge learning (FEEL) enables privacy-preserving model training
through periodic communication between edge devices and the server. Unmanned
Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for
FEEL due to their flexibility and mobility in efficient data collection. In
UAV-assisted FEEL, sensing, computation, and communication are coupled and
compete for limited onboard resources, and UAV deployment also affects sensing
and communication performance. Therefore, the joint design of UAV deployment
and resource allocation is crucial to achieving the optimal training
performance. In this paper, we address the problem of joint UAV deployment
design and resource allocation for FEEL via a concrete case study of human
motion recognition based on wireless sensing. We first analyze the impact of
UAV deployment on the sensing quality and identify a threshold value for the
sensing elevation angle that guarantees a satisfactory quality of data samples.
Due to the non-ideal sensing channels, we consider the probabilistic sensing
model, where the successful sensing probability of each UAV is determined by
its position. Then, we derive the upper bound of the FEEL training loss as a
function of the sensing probability. Theoretical results suggest that the
convergence rate can be improved if UAVs have a uniform successful sensing
probability. Based on this analysis, we formulate a training time minimization
problem by jointly optimizing UAV deployment, integrated sensing, computation,
and communication (ISCC) resources under a desirable optimality gap constraint.
To solve this challenging mixed-integer non-convex problem, we apply the
alternating optimization technique, and propose the bandwidth, batch size, and
position optimization (BBPO) scheme to optimize these three decision variables
alternately.
